Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library

Abstract

Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR placement, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that when using the same number and type of LiDAR, the placement scheme optimized by our proposed method improves the average precision by 15%, compared with the conventional placement scheme in the standard lane scene. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance. Both the RLS Library and related code will be released at https://github.com/PJLab-ADG/PCSim.

7 pages, 6 figures, accepted to the IEEE International Conference on Robotics and Automation (ICRA'23)

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• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
• Semantic Understanding of Foggy Scenes with Purely Synthetic Data
• Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
• Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design
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Cited by in corpus (2)

• Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving
• Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective