This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A GPU Accelerated Particle Filter Based Localization Using 3D Evidential Voxel Maps
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
An evidential theory is widely used for 2D grid-based localization in a robotics field because the theory has benefits to consider additional states such as 'unknown' and 'conflict'. However, there are some problems such as computational limitation and excessive resource share when the localization system is expanded from 2D grid to 3D voxel. In order to overcome the problems, this paper proposes the parallelized particle filter based localization system using 3D evidential voxel maps. A many-core processor based parallel computing framework with optimization techniques is applied to accelerate the computing power. Experiments were performed to evaluate the performance of the localization system in a complex environment, and to compare the computational time and resources between various types of processing units. The experimental results show that the proposed parallel particle filter is much more efficient than particle filter without parallel computing regarding computational cost.
CitationCho, S., Kim, C., Jo, K., and Sunwoo, M., "A GPU Accelerated Particle Filter Based Localization Using 3D Evidential Voxel Maps," SAE Technical Paper 2019-01-0491, 2019, https://doi.org/10.4271/2019-01-0491.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Jo , K. , Jo , Y. , Suhr , J.K. , Jung , H.G. , and Sunwoo , M. Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras IEEE Transactions on Intelligent Transportation Systems 16 6 3377 3392 2015
- Jo , K. and Sunwoo , M. Generation of a Precise Roadway Map for Autonomous Cars IEEE Transactions on Intelligent Transportation Systems 15 3 925 937 2014
- Thrun , S. , Burgard , W. , and Fox , D. A Real-Time Algorithm for Mobile Robot Mapping with Applications to Multi-Robot and 3D Mapping Proceedings 2000 ICRA Millennium Conference IEEE International Conference on Robotics and Automation Symposia Proceedings (Cat No00CH37065); 2000 2000
- Schuetz , M. , Wiyogo , Y. , Schmid , M. , and Dickmann , J. Laser-Based Hierarchical Grid Mapping for Detection and Tracking of Moving Objects Advanced Microsystems for Automotive Applications 2012: Smart Systems for Safe, Sustainable and Networked Vehicles 2012
- Peri , J.S.J. Fundamentals of the Dempster-Shafer Theory Proceedings of SPIE-The International Society for Optical Engineering 2012
- Fairfield , N. and Wettergreen , D. Evidence Grid-Based Methods for 3D Map Matching 2009 IEEE International Conference on Robotics and Automation; 2009 12-17 2009
- Kwak , N. , Stasse , O. , Foissotte , T. , and Yokoi , K. 3D Grid and Particle Based SLAM for a Humanoid Robot 2009 9th IEEE-RAS International Conference on Humanoid Robots 2009
- Hornung , A. , Wurm , K.M. , Bennewitz , M. , Stachniss , C. , and Burgard , W. OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees Autonomous Robots 34 3 189 206 2013
- Li , P. An Efficient Particle Filter-based Tracking Method Using Graphics Processing Unit (GPU) Journal of Signal Processing Systems 68 3 317 332 2012
- Par , K. and Tosun , O. Parallelization of Particle Filter Based Localization and Map Matching Algorithms on Multicore/Manycore Architectures 2011 IEEE Intelligent Vehicles Symposium (IV); 2011 5-9 2011
- Das , S.K. , Mazumdar , C. , and Banerjee , K. GPU Accelerated Novel Particle Filtering Method Computing 96 8 749 773 2014
- Gao , L. , Tang , X. , and Wei , P. Real-Time Implementation of Particle-PHD Filter Based on GPU 2014 IEEE 17th International Conference on Computational Science and Engineering; 2014 19-21 2014
- Smets , P. Analyzing the Combination of Conflicting Belief Functions Information Fusion 8 4 387 412 2007
- Thrun , S. Probabilistic Robotics Commun ACM 45 3 52 57 2002
- Munshi , A , Gaster B , Mattson TG , Fung J , Ginsburg D OpenCL Programming Guide Addison-Wesley Professional 2011 648