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

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.