Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

被引:17
|
作者
Hu, Hanjiang [1 ]
Liu, Zuxin [1 ]
Chitlangia, Sharad [2 ]
Agnihotri, Akhil [3 ]
Zhao, Ding [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Amazon, Seattle, WA USA
[3] Univ Southern Calif, Los Angeles, CA 90007 USA
来源
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) | 2022年
关键词
D O I
10.1109/CVPR52688.2022.00258
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on developing new deep learning algorithms or model architectures, we study the problem from the physical design perspective, i.e., how different placements of multiple LiDARs influence the learning-based perception. To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects. We also present a new data collection, detection model training and evaluation framework in the realistic CARLA simulator to evaluate disparate multi-LiDAR configurations. Using several prevalent placements inspired by the designs of self-driving companies, we show the correlation between our surrogate metric and object detection performance of different representative algorithms on KITTI through extensive experiments, validating the effectiveness of our LiDAR placement evaluation approach. Our results show that sensor placement is non-negligible in 3D point cloud-based object detection, which will contribute to 5% similar to 10% performance discrepancy in terms of average precision in challenging 3D object detection settings. We believe that this is one of the first studies to quantitatively investigate the influence of LiDAR placement on perception performance.
引用
收藏
页码:2540 / 2549
页数:10
相关论文
共 50 条
  • [21] Object detection using depth completion and camera-LiDAR fusion for autonomous driving
    Carranza-Garcia, Manuel
    Javier Galan-Sales, F.
    Maria Luna-Romera, Jose
    Riquelme, Jose C.
    INTEGRATED COMPUTER-AIDED ENGINEERING, 2022, 29 (03) : 241 - 258
  • [22] 3D OBJECT DETECTION FOR AUTONOMOUS DRIVING USING TEMPORAL LIDAR DATA
    McCrae, Scott
    Zakhor, Avideh
    2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2020, : 2661 - 2665
  • [23] 3D vision object detection for autonomous driving in fog using LiDaR
    Tahir, Alishba
    Mumtaz, Rafia
    Irshad, Muhammad Saqib
    SIMULATION MODELLING PRACTICE AND THEORY, 2025, 140
  • [24] MULTI-VIEW FRUSTUM POINTNET FOR OBJECT DETECTION IN AUTONOMOUS DRIVING
    Cao, Pei
    Chen, Hao
    Zhang, Ye
    Wang, Gang
    2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2019, : 3896 - 3899
  • [25] Multi-task learning for dangerous object detection in autonomous driving
    Chen, Yaran
    Zhao, Dongbin
    Lv, Le
    Zhang, Qichao
    INFORMATION SCIENCES, 2018, 432 : 559 - 571
  • [26] SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
    Mohapatra, Sambit
    Mesquida, Thomas
    Hodaei, Mona
    Yogamani, Senthil
    Gotzig, Heinrich
    Maeder, Patrick
    2022 8TH INTERNATIONAL CONFERENCE ON EVENT-BASED CONTROL, COMMUNICATION AND SIGNAL PROCESSING (EBCCSP 2022), 2022,
  • [27] Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving
    Li, Ye
    Hu, Hanjiang
    Liu, Zuxin
    Xu, Xiaohao
    Huang, Xiaonan
    Zhao, Ding
    2024 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2024), 2024, : 9018 - 9025
  • [28] A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems
    Wu, Tao
    Hu, Jun
    Ye, Lei
    Ding, Kai
    SENSORS, 2021, 21 (04) : 1 - 16
  • [29] Implementation of an improved multi-object detection, tracking, and counting for autonomous driving
    Albouchi, Adnen
    Messaoud, Seifeddine
    Bouaafia, Soulef
    Hajjaji, Mohamed Ali
    Mtibaa, Abdellatif
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (18) : 53467 - 53495
  • [30] Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
    Feng, Di
    Cao, Yifan
    Rosenbaum, Lars
    Timm, Fabian
    Dietmayer, Klaus
    2020 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2020, : 871 - 878