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

TL;DR

This paper investigates how the physical placement of multiple LiDAR sensors on autonomous vehicles affects 3D object detection performance, introducing a new evaluation framework and demonstrating placement impacts up to 10% in accuracy.

Contribution

It presents a novel, fast, information-theoretic metric for evaluating LiDAR placement and validates its correlation with detection performance using a new simulation framework.

Findings

Sensor placement significantly affects detection accuracy.

The proposed metric correlates well with actual detection performance.

Optimal placement can improve detection accuracy by up to 10%.

Abstract

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 up 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. The code is available on https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection.