Detailed Analysis on Generating the Range Image for LiDAR Point Cloud Processing

Range images are commonly used representations for 3D LiDAR point cloud in the field of autonomous driving. The approach of generating a range image is generally regarded as a standard approach. However, there do exist two different types of approaches to generating the range image: In one approach, the row of the range image is defined as the laser ID, and in the other approach, the row is defined as the elevation angle. We named the first approach Projection By Laser ID (PBID), and the second approach Projection By Elevation Angle (PBEA). Few previous works have paid attention to the difference of these two approaches. In this work, we quantitatively analyze these two different approaches. Experimental results show that the PBEA approach can obtain much smaller quantization errors than PBID, and should be the preferred choice in reconstruction-related tasks. If PBID is chosen for use in recognition-related tasks, then we have to tolerate its larger quantization error.

[1]  Michael A. Greenspan,et al.  Approximate k-d tree search for efficient ICP , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[2]  Cyrill Stachniss,et al.  Fast range image-based segmentation of sparse 3D laser scans for online operation , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Brendan Englot,et al.  Simulation-based Lidar Super-resolution for Ground Vehicles , 2020, Robotics Auton. Syst..

[4]  Younggun Cho,et al.  Unsupervised Geometry-Aware Deep LiDAR Odometry , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Jake Charland,et al.  LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting , 2021, IEEE Robotics and Automation Letters.

[6]  Ziming Zhang,et al.  A Surface Geometry Model for LiDAR Depth Completion , 2021, IEEE Robotics and Automation Letters.

[7]  Jean-François Aujol,et al.  Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing , 2018, Photogrammetric Engineering & Remote Sensing.

[8]  Hans-Joachim Wuensche,et al.  Enhanced Temporal Data Organization for LiDAR Data in Autonomous Driving Environments , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[9]  Yue Wang,et al.  Pillar-based Object Detection for Autonomous Driving , 2020, ECCV.

[10]  Hao Fu,et al.  Fast Implementation of 3D Occupancy Grid for Autonomous Driving , 2020, 2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[11]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Thomas Brox,et al.  Sparsity Invariant CNNs , 2017, 2017 International Conference on 3D Vision (3DV).

[13]  Cyrill Stachniss,et al.  SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[14]  Tian Xia,et al.  Vehicle Detection from 3D Lidar Using Fully Convolutional Network , 2016, Robotics: Science and Systems.

[15]  Hao Fu,et al.  IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving , 2019, Applied Sciences.

[16]  Adam Herout,et al.  CNN for IMU assisted odometry estimation using velodyne LiDAR , 2017, 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[17]  Sascha Wirges,et al.  Real-time 3D LiDAR Flow for Autonomous Vehicles , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[18]  Lizhuang Ma,et al.  FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds , 2019, 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[19]  Christoph Stiller,et al.  Velodyne SLAM , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[20]  Ji Wan,et al.  Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).