Key technology and application algorithm of intelligent driving vehicle LiDAR

With the preparation of intelligent driving into industrialization and commercialization, LiDAR has become an indispensable environmental sensor with its excellent performance and has developed rapidly in hardware technology and related application algorithms. This paper introduces the key technologies of LiDAR hardware by using LiDAR scanning method and related technology as the entry point, discussing the principle, characteristics and current status of mechanical, hybrid and all-solid-state automotive LiDAR. Three kinds of task-oriented vehicle LiDAR application algorithms, point cloud segmentation, target tracking and recognition, simultaneous location and mapping, are summarized. The analysis shows that the vehicle LiDAR will further become solid-state, intelligent and networked in order to reduce costs, improve performance and meet intelligent driving requirements; the pursuit of application algorithm research is real-time, efficient and reliable.

[1]  Michael S.-C. Lu,et al.  An electrostatically driven 2D micro-scanning mirror with capacitive sensing for projection display , 2015 .

[2]  D. R. Wight,et al.  Novel phased array optical scanning device implemented using GaAs/AlGaAs technology , 1991 .

[3]  Liu Zhong-jie.  MOEMS Microlens Technique and Its Application on Lidar , 2011 .

[4]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[5]  J. P. Siepmann,et al.  Integrable ultra-compact, high-resolution, real-time MEMS LADAR for the individual soldier , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[6]  Chung-De Chen,et al.  A novel two-axis MEMS scanning mirror with a PZT actuator for laser scanning projection. , 2012, Optics express.

[7]  Babak Taati,et al.  Variable Dimensional Local Shape Descriptors for Object Recognition in Range Data , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Christian Laugier,et al.  Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application , 2006, Int. J. Robotics Res..

[9]  Takashi Tsubouchi,et al.  People Tracking Using a Robot in Motion with Laser Range Finder , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Christoph Stiller,et al.  Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[11]  Nico Blodow,et al.  CAD-model recognition and 6DOF pose estimation using 3D cues , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[12]  Ryan Halterman,et al.  Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection , 2010, Defense + Commercial Sensing.

[13]  大卫·S·霍尔.  High definition lidar system , 2007 .

[14]  Mathias Perrollaz,et al.  Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking , 2012, 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV).

[15]  Yu Zhong,et al.  Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[16]  孙建锋,et al.  Electro-optical control two-dimensional laser beam scanning array , 2008 .

[17]  Dirk Wollherr,et al.  A clustering method for efficient segmentation of 3D laser data , 2008, 2008 IEEE International Conference on Robotics and Automation.

[18]  Zhu Zhu Gong Xiao-jin Liu Ji-lin Yang Fei.  Real-time dynamic obstacle detection and tracking using 3D Lidar , 2012 .

[19]  Qiu Qi,et al.  Design on omnidirectional optical system of lidar based on 2D MEMS mirror , 2018 .

[20]  R. Meyer.  Optical beam steering using a multichannel lithium tantalate crystal. , 1972, Applied optics.

[21]  Paul Newman,et al.  Model-free detection and tracking of dynamic objects with 2D lidar , 2015, Int. J. Robotics Res..

[22]  Gary R. Bradski,et al.  Fast 3D recognition and pose using the Viewpoint Feature Histogram , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  张浩 Zhang Hao,et al.  Research Progress of Optical Waveguide Phased Array Scanner , 2014 .

[24]  孙华燕 Sun Huayan,et al.  Advances in Foreign Simulation Softwares of Imaging Laser Radar , 2013 .

[25]  叶青 Ye Qing,et al.  Progress of Optical Phased Array Technology Based on Electro-Optic Material , 2008 .

[26]  J. Hecht.  Lidar for Self-Driving Cars , 2018 .

[27]  Junjie Yao,et al.  A water-immersible 2-axis scanning mirror microsystem for ultrasound andha photoacoustic microscopic imaging applications , 2013, Microsystem technologies : sensors, actuators, systems integration.

[28]  R.H. Hobbs,et al.  Laser Electro-optic Phased Array Devices (LEOPARD) , 1988, Conference Proceedings LEOS Lasers and Electro-Optics Society.

[29]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[30]  Zhang Xi-zhi.  Grid-based Clustering Algorithm with the Parameter Automatization , 2006 .

[31]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[32]  吴 超 Wu Chao,et al.  Design of waveguide optical phased array ladar receiving system , 2016 .

[33]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.