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LiDAR Based Classification Optimization of Localization Policies of Autonomous Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.
The principal of the software stack is to leverage deep learning and powerful filters to perform classification of landmark objects within a scan of the LiDAR. These landmarks can have highly accurate known world coordinates and will be used for position estimation and localization of the vehicle. To develop this framework two phases will have to be conducted. The first will be developing the training set for these landmarks, which will be done by leveraging a dedicated computing platform designed to filter the raw LiDAR data, removing all unnecessary elements leaving only high density clusters which can be used as landmarks. A differential GPS will be used to determine the “true” world coordinates of these landmarks and this data will be attached to the map. The second phase will be deployment of the system on a live vehicle. The sensor suite will consist of a LiDAR, IMU, and standard GPS. The GPS will be used to roughly localize the vehicle to a couple meter distance, from here the proposed framework will search the prior map for the classified elements and do position estimation of the robots position based on the landmark’s associated meta data.
CitationHamieh, I., Myers, R., and Rahman, T., "LiDAR Based Classification Optimization of Localization Policies of Autonomous Vehicles," SAE Technical Paper 2020-01-1028, 2020, https://doi.org/10.4271/2020-01-1028.
Data Sets - Support Documents
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|Unnamed Dataset 2|
- Chen , S. , Li , Y.F. , and Zhang , J. Vision Processing for Realtime 3-d Data Acquisition Based on Coded Structured Light IEEE Transactions on Image Processing 17 2 167 176 2008
- Chen , S. and Li , Y. Automatic Sensor Placement for Model-Based Robot Vision IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34 1 393 408 2004
- Zhang , K. , Chen , S.-C. , Whitman , D. , Shyu , M.-L. et al. A Progressive Morphological Filter for Removing Nonground Measurements from Airborne Lidar Data IEEE Transactions on Geoscience and Remote Sensing 41 4 872 882 2003
- Sithole , G. and Vosselman , G. Filtering of Laser Altimetry Data Using a Slope Adaptive Filter International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 34 3/W4 203 210 2001
- Papadimitriou , S. , Kitagawa , H. , Gibbons , P.B. , and Faloutsos , C. Loci: Fast Outlier Detection Using the Local Correlation Integral Proceedings 19th International Conference on Data Engineering (Cat. No. 03CH37405) IEEE 2003 315 326
- Xu , X. , Song , D. , Zhang , X. et al. Research of Detection of Outliers Based on Locally Linear Weighted Value Computer Science 35 5 154 157 2008
- Breunig , M.M. , Kriegel , H.-P. , Ng , R.T. , and Sander , J. Lof: Identifying Density-Based Local Outliers ACM Sigmod Record 29 93 104 2000
- Chen , S. , Zhang , J. , Li , Y. , and Zhang , J. A Hierarchical Model Incorporating Segmented Regions and Pixel Descriptors for Video Background Subtraction IEEE Transactions on Industrial Informatics 8 1 118 127 2011
- Orts-Escolano , S. , Morell , V. , García-Rodríguez , J. , and Cazorla , M. Point Cloud Data Filtering and Downsampling Using Growing Neural Gas The 2013 International Joint Conference on Neural Networks (IJCNN) IEEE 2013 1 8
- Hamieh , I. , Myers , R. , and Rahman , T. Construction of Autonomous Driving Maps Employing Lidar Odometry 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) May 2019 1 4
- Mucka , P. , Stein , G.J. , and Tobolka , P. Passsenger Ride Comfort and International Roughness Index Specifications in the Slovak Republic Communications-Scientific letters of the University of Zilina 21 1 14 21 2019