Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao
Abstract
The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on au-tonomous vehicles. While most of the existing works focus on developing new deep learning algorithms or model ar-chitectures, we study the problem from the physical design perspective, i.e., how different placements of multiple Li-DARs influence the learning-based perception. To this end, we introduce an easy-to-compute information-theoretic sur-rogate 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 simula-tor 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 exten-sive experiments, validating the effectiveness of our LiDAR placement evaluation approach. Our results show that sen-sor placement is non-negligible in 3D point cloud-based ob-ject detection, which will contribute to 5% ~ 10% performance discrepancy in terms of average precision in chal-lenging 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.