Browse Topic: Lidar

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Landing Site Selection and Visual Scene Generation Based on LiDAR Point Cloud2026-99-18197/17/2026
This paper focuses on autonomous drone landing scenarios. Addressing the core requirements of accurate landing site assessment and intuitive visual presentation, it conducts in-depth research on the application of 3D LiDAR (TOF technology) point cloud data. LiDAR captures point cloud data containing 3D coordinates and reflection intensity values. While sparse, non-uniform, and disordered, its high measurement accuracy and strong anti-interference capabilities make it a key sensor for landing terrain perception. Based on a review of recent research results from related teams, this study designed and implemented a comprehensive technical solution: First, raw point cloud data is acquired via the UDP protocol combined with an SDK interface. Preprocessing is then performed using voxel grid filtering (downsampling) and radius filtering (denoising). The assessment area is then divided into a row-by-column grid. A sliding window method is used to calculate the elevation difference, empty grid
Guo, Hangyu, Shi, Zhe
Validation and Exploration of Multimodal Deep-Learning Camera–LiDAR Calibration Models2026-01-50377/13/2026
This paper presents an innovative study in exploring, evaluating, and implementing deep-learning architectures for the calibration of multimodal sensor systems. The aim of this paper is to leverage the use of sensor fusion to achieve dynamic, real-time alignment between 3D LiDAR and 2D camera sensors. Static calibration methods are tedious and time-consuming, which is why we propose utilizing conventional neural networks (CNNs) coupled with geometrically informed learning to solve this issue. We leverage the foundational principles of extrinsic LiDAR–camera calibration tools such as RegNet, CalibNet, and LCCNet by exploring open-source models that are available online and compare our results with their corresponding research papers. Requirements for extracting these visual and measurable outputs involved tweaking source code, fine-tuning, training, validation, and testing of each of these frameworks for equal comparisons. This approach aims to investigate which of these advanced
Karramreddy, Venkat Sai Raxit, Mitchell, Liam
Microvision fast-tracks adoption of Luminar tech; eyes defense, industrial sectors26TOFHP06_096/1/2026
In the two months since Microvision bought Luminar and acquired key tech and talent, the sensor company has been busy. In that time, they've merged key lidar units from each company and created a perception software stack to run it in a convincing demo of its ADAS and autonomous capabilities. The company is also pushing innovative lidar tech into the defense drone and antidrone markets, already working with a German defense supplier that works with NATO member countries.
Clonts, Chris
Diffusion Framework with Cross-Modality Fusion Perception for Autonomous Driving in Urban Traffic2026-99-07495/15/2026
End-to-end autonomous driving in urban environments faces three core challenges. First, camera and LiDAR sensor heterogeneity causes cross-modal perception inconsistencies and sensor fusion instability. Second, diffusion models suffer from training instability due to scale variance and distribution changes, which limits generalization. Third, traditional trajectory decoders lack structured interaction with semantic elements, thereby undermining planning rationality. To address these issues, CMFPNet introduces an integrated framework with three key modules. The HGCF-Backbone integrates LiDAR and camera features using channel focus, deformable cross-focus, and state space modeling to enhance semantic alignment. The NST module maps physical trajectories to normalized space, employing truncated diffusion sampling for stable generation in just 2–4 steps. The NDA models trajectory generation as a semantic narrative, utilizing a six-stage semantic attention flow incorporating BEV context
Qu, Yanwei, Mo, Hangjie
Flight Test Results for Velocity Aided Navigation in GPS Denied EnvironmentsF-0082-2026-01295/5/2026
This paper presents the flight-test evaluation of a velocity-aided navigation solution that integrates inertial measurements with line-of-sight (LOS) Doppler velocity observations from the Psionic Navigation Doppler Lidar (PNDL) prototype to support navigation in GPS-denied environments. LOS velocity measurements collected during a helicopter flight-test campaign were first compared with velocities derived from an Applanix reference navigation system to assess measurement accuracy. The navigation solution was then developed and evaluated under simulated GPS-denied conditions by removing GPS aiding and continuing operation using LOS velocity measurements alone for extended periods. Results show that Doppler lidar velocity aiding effectively constrains inertial navigation error growth and maintains a stable navigation solution during prolonged GPS outages. These flight-test results demonstrate the utility of FMCW Doppler lidar velocity measurements as an enabling technology for Assured
Hull, Jason, Pierrottet, Diego, Monaco, Jeffrey
A Comparison between Leica RTC360 and Recon-3D Scan Data in Pre- and Post-Collision Vehicle Documentation2026-01-50304/22/2026
Documenting and mapping using three-dimensional (3D) technologies have become essential in crime- and crash-scene investigations in recent years. Traditionally, this has been accomplished using terrestrial laser scanners (TLS), which often come with significant upfront costs. In contrast, Recon-3D, launched in 2022, leverages the capabilities of Apple’s light detection and ranging (LiDAR) sensor, available in Pro and Pro Max models since 2020. This study aims to evaluate the relative accuracy of documenting vehicles in both pre- and post-collision conditions using these technologies. A deviation analysis was conducted utilizing CloudCompare software to compare point cloud data collected from the Leica RTC360 laser scanner with that obtained from Recon-3D for 7 vehicles in a pre- and post-impact condition for a total of n = 14 vehicles. At the 1, 2, and 3 cm deviation thresholds, the average percent of points which fell below each threshold level for all vehicles was 66%, 91%, and 97
Lim, Jihwa, Liscio, Eugene
Self-Supervised Gaussian-Based Framework for Robust Static-Dynamic Decomposition and Real-Time Urban Scene Reconstruction2026-01-00104/7/2026
High-fidelity 3D reconstruction of large-scale urban scenes is critical for autonomous driving perception and simulation. Existing neural rendering methods, including NeRF and Gaussian-based variants, often face challenges like unstable geometry, noisy motion segmentation, and poor performance under sparse viewpoints or varying illumination. This paper presents a self-supervised Gaussian-based framework to address these challenges, enabling robust static–dynamic decomposition and real-time scene reconstruction. The proposed method introduces three innovations: (1) a semantic–geometric feature fusion module that combines semantic context and geometric cues for reliable motion prior estimation; (2) a cross-sequence geometric consistency constraint that enforces depth and surface continuity across time and viewpoints; (3) an efficient Gaussian parameter optimization strategy that stabilizes geometry by jointly constraining scale and normal updates. Experiments on the Waymo Open Dataset
Feng, Runlei, Wang, Ning, Zhang, Zhihao
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-00264/7/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
Enhancing LiDAR Performance under Fog with Adaptive Multi-Echo and Feature-Based Filtering2026-01-00114/7/2026
LiDAR (Light Detection and Ranging) systems are essential for autonomous driving (AD) and advanced driver-assistance systems (ADAS), providing accurate 3D perception of the surrounding environment. However, their performance significantly deteriorates under adverse weather conditions such as fog, where laser pulses are scattered by airborne particles, resulting in substantial noise and reduced ranging accuracy. This scattering effect makes it difficult to detect objects within or behind particulate matter, posing a serious challenge for reliable perception in real-world driving scenarios. To address this issue, we propose an algorithm that combines adaptive multi-echo signal processing with a feature-integrated, rule-based denoising framework to enhance LiDAR performance in noisy environments. The multi-echo approach selectively utilizes meaningful signal returns by evaluating both intensity and relative echo positions. Based on predefined rules, the algorithm identifies the echo most
Kaito, Seiya, Zheng, Shengchao, Fujioka, Ibuki, Beppu, Taro
Development of Technology to Distinguish Contaminants using LiDAR Near-Field Point Cloud based on Real-Field Data2026-01-00204/7/2026
Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling
Kim, Hunjae
Realistic LiDAR Data Simulation for Autonomous Systems using Physics-Informed Learning2026-26-01381/16/2026
Accurate and realistic simulation of LiDAR data is critical for the development and validation of autonomous driving systems. However, existing simulation approaches often suffer from a significant sim-to-real gap due to oversimplified modelling of physical interactions and environmental factors. In this work, we present a physics-informed deep learning framework that bridges this gap by enhancing the realism of simulated LiDAR data using generative adversarial networks guided by domain-specific physical constraints for LiDAR intensity. Our method incorporates key physical factors such as range, surface material properties, angle of incidence, and environmental conditions along with their underlying physical relationships as constraints into the Cycle-Consistent GAN architecture, enabling it to learn realistic transformations from synthetic to real-world LiDAR intensity data without requiring paired samples. We demonstrate the effectiveness of our approach across multiple datasets
Anand, Vivek, Yadav, Sourav, Limba, Mohit, Pandey, Gaurav, Lohani, Bharat
Vehicle Self-Soiling from Exterior Cleaning Mechanisms2026-26-04251/16/2026
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
Mane, Suvidya, Makam, Sri Lalith Madhav, Varghese, Rixson, Desu, Harsha
LLS-SMGSC: A Lidar Localization System Based on Simplified Maps Integrated with Global Search Capabilities2025-01-730312/31/2025
To address the issues of large storage requirements in maps and the dependence of localization accuracy on initial pose estimation, this paper proposes a novel relocalization method named LLS-SMGSC, which is based on simplified maps integrated with Global Search capabilities. Firstly, we partition the map-based on grid size to reduce memory usage. Next, we voxelize the point cloud and map and extract surfel. Then, a coarse-to-fine hierarchical alignment module between the initial frame and maps to estimate the initial global pose. Finally, unmanned platform pose is estimated by the Normal Distribution Transform (ndt) algorithm. Experiments demonstrate that LLS-SMGSC achieves the highest localization accuracy in both unstructured and structured environments while maintaining computational efficiency.
Quan, Zhiheng
3D LiDAR for Improved Rover Traversal and ImageryTBMG-5429512/1/2025
Planetary and lunar rover exploration missions can encounter environments that do not allow for navigation by typical, stereo camera-based systems. Stereo cameras meet difficulties in areas with low ambient light (even when lit by floodlights), direct sunlight, or washed-out environments. Improved sensors are required for safe and successful rover mobility in harsh conditions. NASA Goddard Space Flight Center has developed a Space Qualified Rover LiDAR (SQRLi) system that will improve rover sensing capabilities in a small, lightweight package. The new SQRLi package is developed to survive the hazardous space environment and provide valuable image data during planetary and lunar rover exploration.
LIPO: Lidar Inertial Odometry for ICP Comparison2025-01-04399/16/2025
We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark
Mick, Darwin, Pool, Taylor, Nagaraju, Madankumar Sathenahally, Kaess, Michael, Choset, Howie, Travers, Matthew
VORA™: Vision for Off-Road Autonomy2025-01-04419/16/2025
The Vision for Off-road Autonomy (VORA) project used passive, vision-only sensors to generate a dense, robust world model for use in off-road navigation. The research resulted in vision-based algorithms applicable to defense and surveillance autonomy, intelligent agricultural applications, and planetary exploration. Passive perception for world modeling enables stealth operation (since lidars can alert observers) and does not require more expensive or specialized sensors (e.g., radar or lidar). Over the course of this three-phase program, SwRI built components of a vision-only navigation pipeline and tested the result on a vehicle platform in an off-road environment.
Towler, Meera Day, Garza, Harold A., Chambers, David R.
A 3D Visibility Label for Mobility Coatings: Enhancing Traffic Safety Through Color and Sensor Perception12-09-02-00097/22/2025
The mobility industry is rapidly advancing towards more autonomous modes of transportation with the adoption of sophisticated self-driving technologies. However, a critical challenge, being the lack of standardized norms for defining, measuring, and ensuring vehicle visibility across various dynamic traffic environments, remains. This lack of awareness of visibility is hindering the development of new regulations for vehicle visibility and the controlled transition to a fully-integrated autonomous future. While current efforts focus on improving sensing technologies like computer vision, LiDAR systems, and sensor fusion development, two key issues remain unresolved: 1 The absence of a representative and realistic three-dimensional color visibility model for measuring and comparing the visibility of complex shapes with large but varying color coated three-dimensional surface areas. 2 The need for enhanced visibility solutions that improve visibility and vehicle detectability for all
Mijnen, Paul W., Moerenburg, Joost H.
U-Shift IV: Innovations and Challenges in Autonomous Urban Mobility2025-01-02847/2/2025
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
Pohl, Eric, Scheibe, Sebastian, Münster, Marco, Osebek, Manuel, Kopp, Gerhard, Siefkes, Tjark
Customized Signalized Intersection Control for Pedestrian Safety Enhancement through Computer Vision Technologies09-13-02-00086/26/2025
The escalating complexity at intersections challenges the safety of the interaction between vehicles and pedestrians, especially for those with mobility impairments. Traditional traffic control systems detect pedestrians through costly technologies such as LiDAR and radar, limiting their adoption due to high costs and static programming. Therefore, the article proposes a customized signalized intersection control (CSIC) algorithm for pedestrian safety enhancement. This algorithm integrates advanced computer vision (CV) algorithms to detect, track, and predict pedestrian movements in real time, enhancing safety at a signalized intersection while remaining economically viable and easily integrated into existing infrastructure. Implemented at a key intersection in Bellevue, the CSIC system achieves a 100% pedestrian passing rate while simultaneously minimizing the average remaining walk time after crossings. The algorithm used in this study demonstrates the potential of combining CV with
Xia, Rongjing, Fang, Hongchao, Zhang, Chenyang
Reducing LiDAR Cabin Noise with Damped Elastomers a Novel Method for Predicting Performance and Durability2025-01-01145/5/2025
Mechanical light detection and ranging (LiDAR) units utilize spinning lasers to scan surrounding areas to enable limited autonomous driving. The motors within the LiDAR modules create vibration that can propagate through the vehicle frame and become unwanted noise in the cabin of a vehicle. Decoupling the module from the body of the vehicle with highly damped elastomers can reduce the acoustic noise in the cabin and improve the driving experience. Damped elastomers work by absorbing the vibrational energy and dispelling it as low-grade heat. By creating a unique test method to model the behavior of the elastomers, a predictable pattern of the damping ratio yielded insight into the performance of the elastomer throughout the operating temperature range of the LiDAR module. The test method also provides an objective analysis of elastomer durability when exposed to extreme temperatures and loading conditions for extended periods of time. Confidence in elastomer behavior and life span was
Russell, Casey, Masterson, Peter, O'Connell, Kerry
Accuracy of Mobile Phone LiDAR on Crashed Vehicles2025-01-86924/1/2025
Apple’s mobile phone LiDAR capabilities can be used with multiple software applications to capture the geometry of vehicles and smaller objects. The results from different software have been previously researched and compared to traditional ground-based LiDAR. However, results were inconsistent across software applications, with some software being more accurate and others being less accurate. (Technical Paper 2023-01-0614. Miller, Hashemian, Gillihan, Benes.) This paper builds upon existing research by utilizing the updated LiDAR hardware that Apple has added to its iPhone 15 smartphone lineup. This new hardware, in combination with the software application PolyCam, was used to scan a variety of crashed vehicles. These crashed vehicles were also scanned using a FARO 3D scanners and Leica RTC 360 scanners, which have been researched extensively for their accuracy. The PolyCam scans were compared to FARO and Leica scans to determine accuracy for point location and scaling. Previous
Miller, Seth Higgins, Stogsdill, Michael, McWhirter, Seth
LiDAR Contamination Recognition Based on Optimized PointNet2025-01-80134/1/2025
LiDAR sensors have become an integral component in the realm of autonomous driving, widely utilized in environmental perception and vehicle navigation. However, in real-world road environments, contaminants such as dust and dirt can severely hamper the cleanliness of LiDAR optical windows, thereby degrading operational performance and affecting the overall environmental perception capabilities of intelligent driving systems. Consequently, maintaining the cleanliness of LiDAR optical windows is crucial for sustaining device performance. Unfortunately, the scarcity of publicly available LiDAR contamination datasets poses a challenge to the research and development of contamination identification algorithms. This paper first introduces a method for acquiring LiDAR-pollution datasets. LiDAR data acquisition on urban open roads simulates different types of pollution, including mud and leaves. The constructed dataset meticulously differentiates among the three states with clear labels: no
Wei, Ziyu, Quo, Binyun, Lujia, Ran, Li, Liguang
Decentralized Perception System with Multiple Viewpoints2025-01-80974/1/2025
Vehicle-to-Infrastructure (V2I) cooperation has emerged as a fundamental technology to overcome the limitations of the individual ego-vehicle perception. Onboard perception is limited by the lack of information for understanding the environment, the lack of anticipation, the drop of performance due to occlusions and the physical limitations of embedded sensors. The perception of V2I in a cooperative manner improves the perception range of the ego vehicle by receiving information from the infrastructure that has another point of view, mounted with sensors, such as camera and LiDAR. This technical paper presents a perception pipeline developed for the infrastructure based on images with multiple viewpoints. It is designed to be scalable and has five main components: the image acquisition for the modification of camera settings and to get the pixel data, the object detection for fast and accurate detection of four wheels, two wheels and pedestrians, the data fusion module for robust
Picard, Quentin, Morice, Malo, Fadili, Maryem, Pechberti, Steve
Validating the Sun System in 3ds Max for Recreating Shadows2025-01-86854/1/2025
Shadow positions can be useful in determining the time of day that a photograph was taken and determining the position, size, and orientation of an object casting a shadow in a scene. Astronomical equations can predict the location of the sun relative to the earth, and therefore the position of shadows cast by objects, based on the location’s latitude and longitude as well as the date and time. 3D computer software includes these calculations as a part of their built-in sun systems. In this paper, the authors examine the sun system in the 3D modeling software 3ds Max to determine its accuracy for use in accident reconstruction. A parking lot was scanned using a FARO LiDAR scanner to create a point cloud of the environment. A camera was then set up on a tripod at the environment, and photographs were taken at various times throughout the day from the same location. This environment was 3D modeled in 3ds Max based on the point cloud, and the sun system in 3ds Max was configured using the
Barreiro, Evan, Erickson, Michael, Smith, Connor, Carter, Neal, Hashemian, Alireza
Hierarchical Feature-Based Localization Using Scene Graphs in Off-Road Navigation2025-01-80464/1/2025
While numerous advancements have been made in autonomous navigation for structured indoor and outdoor environments, these solutions often do not generalize well to off-road settings. There are unique challenges in such settings such as unreliable GPS, limited computational and memory resources, and sparse environmental features, making navigation particularly difficult. In our work, we propose a novel data structure called Hierarchical Dynamic Scene Graphs (HDSG) to address these challenges. HDSG captures environmental information at different resolutions, integrating both geometric and semantic features. It enables various navigation tasks such as localization, loop closure, and human interaction through the visualization of environmental features for remote operators. We evaluated the performance of localizing a robot’s position within the world frame by comparing compact spatial descriptors extracted from semi-consecutive scene graphs, derived from 3D LiDAR point clouds. Compared to
Alam, Fardifa Fathmiul, Luricich, Federico, Li, Nianyi, Jia, Yunyi, Li, Bing
A Motion-Aware Continuous Time LiDAR-Inertial SLAM Framework2025-01-80444/1/2025
Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their robustness under any light conditions. However, even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, as well as high motion of the robots. In this paper, we present a motion-aware continuous-time LiDAR-inertial SLAM framework. We introduce an efficient EKF-ICP sensor fusion solution by loosely coupling poses from the continuous time ICP and IMU data, designed to improve convergence speed and robustness over existing methods while incorporating a sophisticated motion constraint to maintain accurate localization during rapid motion changes. Our framework is evaluated on the KITTI datasets and artificially motion-induced dataset sequences, demonstrating
Kokenoz, Cigdem, Shaik, Toukheer, Sharma, Abhishek, Pisu, Pierluigi, Li, Bing
Development of a Rule-Based Lidar Lane-Keeping Assistance System for Autonomous Vehicles Applications2025-01-80254/1/2025
Lane-keeping is critical for SAE Level 3+ autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S.-approved level 3 vehicles are equipped with lidar, likely for accelerating active safety. Lidar offers direct distance measurements, allowing rule-based algorithms compared to camera-based methods, which rely on statistical methods for perception. Furthermore, lidar can support a more comprehensive and detailed approach to studying lane-keeping. This paper proposes a module perceiving oncoming vehicle behavior, as part of a larger behavior-tree structure for adaptive lane-keeping using data from a lidar sensor. The complete behavior tree would include road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the lane-keeping behavior, type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm was evaluated using
Soloiu, Valentin, Mehrzed, Shaen, Kroeger, Luke, Pierce, Kody, Sutton, Timothy, Lange, Robin
Driving in the Rain: Evaluating How Surface Material Properties Affect LiDAR Perception in Autonomous Driving2025-01-80164/1/2025
Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this
Pao, Wing Yi, Li, Long, Agelin-Chaab, Martin, Roy, Langis, Knutzen, Julian, Baltazar Y Jimenez, Alexis, Muenker, Klaus, Chakraborty, Anirban, Komar, John
A Comparison of UAV LiDAR and Terrestrial Laser Scanner Accuracies2025-01-86944/1/2025
The accident reconstruction community frequently uses Terrestrial LiDAR (TLS) to capture accurate 3D images of vehicle accident sites. This paper compares the accuracy, workflow, benefits, and challenges of Unmanned Aerial Vehicle (UAV) LiDAR, or Airborne Laser Scanning (ALS), to TLS. Two roadways with features relevant to accident reconstruction were selected for testing. ALS missions were conducted at an altitude of 175 feet and a velocity of 4 miles per hour at both sites, followed by 3D scanning using TLS. Survey control points were established to minimize error during cloud-to- cloud TLS registration and to ensure accurate alignment of ALS and TLS point clouds. After data capture, the ALS point cloud was analyzed against the TLS point cloud. Approximately 80% of ALS points were within 1.8 inches of the nearest TLS point, with 64.8% at the rural site and 59.7% at the suburban site within 1.2 inches. These findings indicate that UAV-based LiDAR can achieve comparable accuracy to TLS
Foltz, Steven, Terpstra, Toby, Clarson, Julia
A Step Toward More Accurate 3D Object DetectionTBMG-528154/1/2025
Most 3D object detection methods employ LiDAR sensors to create 3D point clouds of their environment. Simply put, LiDAR sensors use laser beams to rapidly scan and measure the distances of objects and surfaces around the source. However, using LiDAR data alone can lead to errors due to the high sensitivity of LiDAR to noise, especially in adverse weather conditions like during rainfall.
Background Filtering and Target Detection for Traffic Objects with Roadside LiDAR Point Cloud Data2025-01-71933/19/2025
Roadside perception technology is an essential component of traffic perception technology, primarily relying on various high-performance sensors. Among these, LiDAR stands out as one of the most effective sensors due to its high precision and wide detection range, offering extensive application prospects. This study proposes a voxel density-nearest neighbor background filtering method for roadside LiDAR point cloud data. Firstly, based on the relatively fixed nature of roadside background point clouds, a point cloud filtering method combining voxel density and nearest neighbor is proposed. This method involves voxelizing the point cloud data and using voxel grid density to filter background point clouds, then the results are processed through a neighbor point frame sequence to calculate the average distance of the specified points and compare with a distance threshold to complete accurate background filtering. Secondly, a VGG16-Pointpillars model is proposed, incorporating a CNN
Liu, Zhiyuan, Rui, Yikang
A Method for Constructing Software and Hardware of a Multi-Source Fusion Intelligent Driving2025-01-71583/19/2025
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
Zhan, KaiDi, Gao, Chengfa, Xu, Dawei, Lan, Minyi, Ding, Rongjing
Multi-Stage Registration and Feature Based Robust and Precise LiDAR SLAM2025-01-71612/21/2025
In a complex and ever-changing environment, achieving stable and precise SLAM (Simultaneous Localization and Mapping) presents a significant challenge. The existing SLAM algorithms often exhibit limitations in design that restrict their performance to specific scenarios; they are prone to failure under conditions of perceptual degradation. SLAM systems should maintain high robustness and accurate state estimation across various environments while minimizing the impact of noise, measurement errors, and external disturbances. This paper proposes a three-stage method for registering LiDAR point cloud. First, the multi-sensor factor graph is combined with historical pose and IMU pre-integration to provide a priori pose estimation; then a new method for extracting planar features is used to describe and filter the local features of the point cloud. Second, the normal distribution transform (NDT) algorithm is used as coarse registration. Third, the feature to feature registration is used for
Li, Zhichao, Tong, Panpan, Shi, Weigang, Bi, Xin
Cryptographic Method for Secure Object Segmentation for Autonomous Driving Perception Systems12-08-01-00082/14/2025
The modern-day vehicle’s driverless or driver-assisted systems are developed by sensing the surroundings using a combination of camera, lidar, and other related sensors by forming an accurate perception of the driving environment. Machine learning algorithms help in forming perception and perform planning and control of the vehicle. The control of the vehicle which reflects safety depends on the accurate understanding of the surroundings by the trained machine learning models by subdividing a camera image fed into multiple segments or objects. The semantic segmentation system comes with the objective of assigning predefined class labels such as tree, road, and the like to each pixel of an image. Any security attacks on pixel classification nodes of the segmentation systems based on deep learning result in the failure of the driver assistance or autonomous vehicle safety functionalities due to a falsely formed perception. The security compromisations on the pixel classification head of
Prashanth, K.Y., Rohitha , U.M.
VTOL UAS Auto-recovery Using a Tested Long-Term Motion Prediction Method to Define the DeckSM_AVTOL_2025-53152/3/2025
Launch, recovery, and deck handling operational performance on smaller ship platforms like Corvettes, Frigates and Destroyers are qualified as the most challenging tasks in the UAS ship-deployment of a VTOL Uncrewed Air System (UAS). One of the main hurdles is the random nature of seaway-created deck motions coupled with ship structure disturbed air wake patterns. The MoD has supported a range of work aimed at bringing Quiescent Period Prediction (QPP) technology to fruition. QPP firstly requires Wave Profiling RADAR to measure the sea wave system out to approximately 2km in the region around a vessel. Secondly these measurements are employed in a wave propagation model to predict the actual wave forces acting on a vessel. Using the wave predictions as inputs to a vessel model makes possible to predict the actual (deterministic as opposed to statistical) motions of a vessel. Wave systems naturally alternate groups of large waves with smaller waves, this property, combined with the
Ferrier, Bernard, Watson, B., Belmont, M.R., Christmas, J.T.
Adaptive Localization Algorithm for Autonomous Mining Trucks with Motion Distortion Removal2025-01-70271/31/2025
During the operation of autonomous mining trucks in the process of crushing stones, the GPS signal is lost due to signal blockage by the crushing workshop. Simultaneous Localization and Mapping (SLAM) becomes critical for ensuring accurate vehicle positioning and smooth operation. However, the bumpy road conditions and the scarcity of plane and corner feature points in mining environments pose challenges to SLAM algorithms in practical applications, such as pose jumps and insufficient positioning accuracy. To address this, this paper proposes a high-precision positioning algorithm based on inertial navigation 3D signals, incorporating point cloud motion distortion correction, a vehicle roll model, and an Adaptive Kalman Filter (AKF). The goal is to improve the positioning accuracy and stability of autonomous mining trucks in complex scenarios. This paper utilizes real-world operational data from mining vehicles and adopts a 3D point cloud motion distortion correction algorithm to
Meng, Chunyang, Song, Kang, Xie, Hui, Xing, Wanyong
Data-Driven Performance Evaluation of Machine Learning for Velocity Estimation Based on Scan Artifacts from LiDAR Sensors12-08-04-00321/21/2025
Light detection and ranging (LiDAR) sensors are increasingly applied to automated driving vehicles. Microelectromechanical systems are an established technology for making LiDAR sensors cost-effective and mechanically robust for automotive applications. These sensors scan their environment using a pulsed laser to record a point cloud. The scanning process leads in the point cloud to a distortion of objects with a relative velocity to the sensor. The consecutive generation and processing of points offers the opportunity to enrich the measured object data from the LiDAR sensors with velocity information by extracting information with the help of machine learning, without the need for object tracking. Turning it into a so-called 4D-LiDAR. This allows object detection, object tracking, and sensor data fusion based on LiDAR sensor data to be optimized. Moreover, this affects all overlying levels of autonomous driving functions or advanced driver assistance systems. However, since such
Haas, Lukas, Haider, Arsalan, Kastner, Ludwig, Kuba, Matthias, Zeh, Thomas, Jakobi, Martin, Koch, Alexander Walter
SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer134951/10/2025
In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in
Hineman, Marcie
Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems2024-01-702812/13/2024
Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructure to address the occlusion and sensing range limitation issues. To date, existing research is mainly focused on prototyping cooperative perception using only one type of sensor such as LiDAR and camera. In such cases, the performance of cooperative perception is constrained by individual sensor limitations. To exploit the multi-modality of sensors to further improve distant object detection accuracy, in this paper, we propose a unified multi-modal multi-agent cooperative perception framework that integrates camera and LiDAR data to enhance perception performance in intelligent transportation systems. By leveraging the complementary strengths of LiDAR and camera sensors, our framework utilizes the geometry information from LiDAR and the semantic information from cameras to achieve an accurate cooperative perception
Meng, Zonglin, Xia, Xin, Zheng, Zhaoliang, Gao, Letian, Liu, Wei, Zhu, Jiaqi, Ma, Jiaqi
Autonomous Mobile Robot for Gas Leak Localization2024-01-522612/10/2024
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
Feroz Ali, L., Madhankumar, S., Hariush, V.C., Jahath Pranav, R., Jayadeep, J., Jeffrey, S.
AI-Based Smart Vehicle Parking System2024-01-524212/10/2024
This project presents the development of an advanced Autonomous Mobile Robot (AMR) designed to autonomously lift and maneuver four-wheel drive vehicles into parking spaces without human intervention. By leveraging cutting-edge camera and sensor technologies, the AMR integrates LIDAR for precise distance measurements and obstacle detection, high-resolution cameras for capturing detailed images of the parking environment, and object recognition algorithms for accurately identifying and selecting available parking spaces. These integrated technologies enable the AMR to navigate complex parking lots, optimize space utilization, and provide seamless automated parking. The AMR autonomously detects free parking spaces, lifts the vehicle, and parks it with high precision, making the entire parking process autonomous and highly efficient. This project pushes the boundaries of autonomous vehicle technology, aiming to contribute significantly to smarter and more efficient urban mobility systems.
Atheef, M. Syed, Sundar, K. Sham, Kumar, P. P. Prem, Karthika, J.
Best Practices in Sensor Selection for Object Detection in Autonomous Driving: A Practitioner’s Perspective2024-28-021812/5/2024
Object detection (OD) is one of the most important aspects in Autonomous Driving (AD) application. This depends on the strategic sensor’s selection and placement of sensors around the vehicle. The sensors should be selected based on various constraints such as range, use-case, and cost limitation. This paper introduces a systematic approach for identifying the optimal practices for selecting sensors in AD object detection, offering guidance for those looking to expand their expertise in this field and select the most suitable sensors accordingly. In general, object detection typically involves utilizing RADAR, LiDAR, and cameras. RADAR excels in accurately measuring longitudinal distances over both long and short ranges, but its accuracy in lateral distances is limited. LiDAR is known for its ability to provide accurate range data, but it struggles to identify objects in various weather conditions. On the other hand, camera-based systems offer superior recognition capabilities but lack
Maktedar, Asrarulhaq, Chatterjee, Mayurika
INTEGRATING SENSOR DATA: Selecting an ADAS decision-making process24AUTP12_0112/1/2024
Exactly when sensor fusion occurs in ADAS operations, late or early, impacts the entire system. Governments have been studying Advanced Driver Assistance Systems (ADAS) since at least the late 1980s. Europe's Generic Intelligent Driver Support initiative ran from 1989 to 1992 and aimed “to determine the requirements and design standards for a class of intelligent driver support systems which will conform with the information requirements and performance capabilities of the individual drivers.” Automakers have spent the past 30 years rolling out such systems to the buying public. Toyota and Mitsubishi started offering radar-based cruise control to Japanese drivers in the mid-1990s. Mercedes-Benz took the technology global with its Distronic adaptive cruise control in the 1998 S-Class. Cadillac followed that two years later with FLIR-based night vision on the 2000 Deville DTS. And in 2003, Toyota launched an automated parallel parking technology called Intelligent Parking Assist on the
Ramsey, Jonathon
Research on the Application of High-Static-Low-Dynamic Stiffness Vibration Isolator in Electro-Optical Pods of Unmanned Aerial Vehicles2024-01-701511/15/2024
With the rapid advancement in unmanned aerial vehicle (UAV) technology, the demand for stable and high-precision electro-optical (EO) pods, such as cameras, lidar sensors, and infrared imaging systems, has significantly increased. However, the inherent vibrations generated by the UAV’s propulsion system and aerodynamic disturbances pose significant challenges to the stability and accuracy of these payloads. To address this issue, this paper presents a study on the application of high-static low-dynamic stiffness (HSLDS) vibration isolation devices in EO payloads mounted on UAVs. The HSLDS system is designed to effectively isolate low-frequency and high-amplitude vibrations while maintaining high static stiffness, ensuring both stability during hovering and precise pointing capabilities. A nonlinear dynamic system model with two degrees of freedom is formulated for an EO pod supported by HSLDS isolators at both ends. The model’s natural frequencies are determined, and approximate
Tian, Yishen, Guo, Gaofeng, Wang, Guangzhao, Wei, Wan, Bao, Lingcong, Dong, Guan, Li, Liujie
Autonomous Landing of UAV in Non-Cooperative Environments Based on Fusion of Monocular and Stereo Depth Estimation2024-01-701911/15/2024
In non-cooperative environments, unmanned aerial vehicles (UAVs) have to land without artificial markers, which is a key step towards achieving full autonomy. However, the existing vision-based schemes have the common problems of poor robustness and generalization, and the LiDAR-based schemes have the disadvantages of low resolution, high power consumption and high weight. In this paper, we propose an UAV landing system equipped with a binocular camera to preform 3D reconstruction and select the safe landing zone. The whole system only consists of a stereo camera, and the innovation of the solution is fusing the stereo matching algorithm and monocular depth estimation(MDE) model to get a robust prediction on the metric depth. The whole landing system consists of a stereo matching module, a monocular depth estimation (MDE) module, a depth fusion module, and a safe landing zone selection module. The stereo matching module uses Semi-Global Matching (SGM) algorithm to calculate the
Zhou, YiBiao, Zhang, BiHui
Why Honda Wants FMCW LiDAR in Next Generation MobilityTBMG-5191911/1/2024
In September, after several months of evaluating the market, “Honda Xcelerator Ventures” — the automotive manufacturer’s startup investment subsidiary — made a major investment award to California-based silicon photonics startup SiLC Technologies, Inc., to develop next generation Frequency-Modulated Continuous Wave (FMCW) LiDAR for “all types of mobility.”
Towards Automation of Reference Data Generation for ADAS/AD Functions Development – ALiVA Framework2024-28-002210/17/2024
The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video & point-cloud data. ALiVA is capable of processing large volumes of camera & lidar sensor data. Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms and annotation results review functionality. Automatic object detection functionality creates a precise bounding box around the area of interest and assigns class labels to annotated objects. Object tracking algorithms tracks detected objects in video frames, provides a unique object id for each object and
Mardhekar, Amogh, Pawar, Rushikesh, Mohod, Rucha, Shirudkar, Rohit, Hivarkar, Umesh N.
A Powerful Vision-Based Autonomy Alternative to LiDAR, Radar, GPSTBMG-5174710/1/2024
Southwest Research Institute has developed off-road autonomous driving tools with a focus on stealth for the military and agility for space and agriculture clients. The vision-based system pairs stereo cameras with novel algorithms, eliminating the need for LiDAR and active sensors.
Approach for Extrinsic Calibration of a Light Detection and Ranging Sensor and a Monocular Camera Using Bounding Boxes2024-01-50929/4/2024
Sensor calibration plays an important role in determining overall navigation accuracy of an autonomous vehicle (AV). Calibrating the AV’s perception sensors, typically, involves placing a prominent object in a region visible to the sensors and then taking measurements to further analyses. The analysis involves developing a mathematical model that relates the AV’s perception sensors using the measurements taken of the prominent object. The calibration process has multiple steps that require high precision, which tend to be tedious and time-consuming. Worse, calibration has to be repeated to determine new extrinsic parameters whenever either one of the sensors move. Extrinsic calibration approaches for LiDAR and camera depend on objects or landmarks with distinct features, like hard edges or large planar faces that are easy to identify in measurements. The current work proposes a method for extrinsically calibrating a LiDAR and a forward-facing monocular camera using 3D and 2D bounding
Omwansa, Mark, Sharma, Sachin, Meyer, Richard, Brown, Nicholas
Q&A24AUTP08_148/1/2024
To round out this issue's cover story, we spoke with Clement Nouvel, Valeo's chief technical officer for lidar, about Valeo's background in ADAS and what's coming next. Nouvel leads over 300 lidar engineers and the company's third-generation Scala 3 lidar is used on production vehicles from European and Asian automakers. The Scala 3 sensor system scans the area around a vehicle 25 times per second, can detect objects more than 200 meters (656 ft) away with a wide field of vision and operates at speeds of up to 130 km/h (81 mph) on the highway. In 2023, Valeo secured two contracts for Scala 3, one with an Asian manufacturer and the other with a “leading American robotaxi company,” Valeo said in its most-recent annual report. Valeo has now received over 1 billion euros (just under $1.1 billion) in Scala 3 orders. Also in 2023, Valeo and Qualcomm agreed to jointly supply connected displays, clusters, driving assistance technologies and, importantly, sensor technology for to two- and three
Dinkel, John
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