Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps

Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.

[1]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[2]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[3]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[4]  Takeo Kanade,et al.  Terrain mapping for a roving planetary explorer , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[5]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[7]  Wolfram Burgard,et al.  Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Wolfram Burgard,et al.  OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems , 2010 .

[9]  Wolfram Burgard,et al.  Hierarchies of octrees for efficient 3D mapping , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[11]  Juan Andrade-Cetto,et al.  Segmentation of Dynamic Objects from Laser Data , 2011, ECMR.

[12]  Erik Einhorn,et al.  Finding the adequate resolution for grid mapping - Cell sizes locally adapting on-the-fly , 2011, 2011 IEEE International Conference on Robotics and Automation.

[13]  Reid G. Simmons,et al.  Background subtraction and accessibility analysis in evidence grids , 2011, 2011 IEEE International Conference on Robotics and Automation.

[14]  Paul Newman,et al.  Practice makes perfect? Managing and leveraging visual experiences for lifelong navigation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  Laurent Itti,et al.  Finding planes in LiDAR point clouds for real-time registration , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Thierry Bouwmans,et al.  Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..

[17]  Ryan M. Eustice,et al.  Visual localization within LIDAR maps for automated urban driving , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Maarten Weyn,et al.  A Survey of Rigid 3D Pointcloud Registration Algorithms , 2014 .

[19]  Paul Newman,et al.  Leveraging experience for large-scale LIDAR localisation in changing cities , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Erik Nelson.  Environment Model Adaptation for Autonomous Exploration , 2015 .

[21]  Arthur Zimek,et al.  Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection , 2015, ACM Trans. Knowl. Discov. Data.

[22]  Paulo Peixoto,et al.  Two-Stage Static/Dynamic Environment Modeling Using Voxel Representation , 2015, ROBOT.

[23]  Ryan W. Wolcott,et al.  Robust Localization in 3D Prior Maps for Autonomous Driving , 2016 .

[24]  John J. Leonard,et al.  Towards lifelong feature-based mapping in semi-static environments , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Paulo Peixoto,et al.  3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes , 2016, Robotics Auton. Syst..

[26]  Oliver Grau,et al.  VConv-DAE: Deep Volumetric Shape Learning Without Object Labels , 2016, ECCV Workshops.

[27]  Christian Berger,et al.  Mastering data complexity for autonomous driving with adaptive point clouds for urban environments , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[28]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[29]  Brendan Englot,et al.  Bayesian generalized kernel inference for occupancy map prediction , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Martin Jägersand,et al.  MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving , 2017, ArXiv.

[31]  Anath Fischer,et al.  3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Marc Pollefeys,et al.  Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark , 2017, ArXiv.

[33]  S. Yogamani,et al.  Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians , 2017, ArXiv.

[34]  Jian Yang,et al.  Lidar-histogram for fast road and obstacle detection , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[35]  Ryan M. Eustice,et al.  A learning approach for real-time temporal scene flow estimation from LIDAR data , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[36]  posudek oponenta,et al.  LIDAR Based Sequential Registration and Mapping for Autonomous Vehicles , 2017 .

[37]  Ravi Teja Sukhavasi,et al.  An end-to-end system for crowdsourced 3D maps for autonomous vehicles: The mapping component , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[38]  Leland McInnes,et al.  hdbscan: Hierarchical density based clustering , 2017, J. Open Source Softw..

[39]  Bastian Leibe,et al.  Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[40]  Ying Li,et al.  Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review , 2018, Remote. Sens..

[41]  H. Groß,et al.  Complex-YOLO: Real-time 3D Object Detection on Point Clouds , 2018, ArXiv.

[42]  Ruigang Yang,et al.  DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[43]  Alberto L. Sangiovanni-Vincentelli,et al.  A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving , 2018, ICMR.

[44]  Senthil Yogamani,et al.  Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[45]  Adam Herout,et al.  CNN for very fast ground segmentation in velodyne LiDAR data , 2017, 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).