LiDAR配置最適化のための知覚エントロピー指標の提案
A Novel Perception Entropy Metric for Optimizing Vehicle Perception with LiDAR Deployment
LiDARの点群分布が車両検出性能に与える影響を確率占有グリッドに基づくエントロピー指標で評価し、差分進化と粒子群最適化でLiDAR配置を最適化する手法を提案した。
著者: Yongjiang He, Peng Cao, Zhongling Su, Xiaobo Liu
分類: cs.RO, cs.IT, math.IT
原文アブストラクト
Developing an effective evaluation metric is crucial for accurately and swiftly measuring LiDAR perception performance. One major issue is the lack of metrics that can simultaneously generate fast and accurate evaluations based on either object detection or point cloud data. In this study, we propose a novel LiDAR perception entropy metric based on the probability of vehicle grid occupancy. This metric reflects the influence of point cloud distribution on vehicle detection performance. Based on this, we also introduce a LiDAR deployment optimization model, which is solved using a differential evolution-based particle swarm optimization algorithm. A comparative experiment demonstrated that the proposed PE-VGOP offers a correlation of more than 0.98 with vehicle detection ground truth in evaluating LiDAR perception performance. Furthermore, compared to the base deployment, field experiments indicate that the proposed optimization model can significantly enhance the perception capabilities of various types of LiDARs, including RS-16, RS-32, and RS-80. Notably, it achieves a 25% increase in detection Recall for the RS-32 LiDAR.
