Recent 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 novel model architectures to process point cloud data, we study the problem from an optimal sensing perspective. To this end, together with a fast evaluation function based on ray tracing within the perception region of a LiDAR configuration, we propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. We show a correlation between our surrogate function and common object detection performance metrics. We demonstrate the efficacy of our approach by verifying our results in a robust and reproducible data collection and extraction framework based on the CARLA simulator. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% ~ 20% on the state-of-the-art perception algorithms. We believe that this is one of the first studies to use LiDAR placement to improve the performance of perception.

中文翻译：

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通过传感器放置改善感知能力：为自动驾驶汽车设计Multi-LiDAR系统

近年来，人们对提高LiDAR在自动驾驶汽车上的感知性能的兴趣日益浓厚。尽管大多数现有工作都集中在开发新颖的模型体系结构以处理点云数据，但我们还是从最佳的传感角度研究了该问题。为此，结合基于激光雷达在LiDAR配置感知区域内的光线跟踪的快速评估功能，我们提出了一种基于概率占用网格（POG）的易于计算的信息理论代理成本度量，以优化LiDAR放置最大感测。我们显示了代理功能和常见对象检测性能指标之间的相关性。通过在基于CARLA模拟器的可靠且可重现的数据收集和提取框架中验证我们的结果，我们证明了该方法的有效性。我们的结果证实，传感器放置是基于3D点云的对象检测的重要因素，并且在最新的感知算法上可能导致性能变化10％〜20％。我们认为，这是使用LiDAR放置来改善感知性能的首批研究之一。