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/LiDARSimLib-and-Placement-Evaluation.Comment: 7 pages, 6 figures, accepted to the IEEE International Conference on
Robotics and Automation (ICRA'23