The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages. Latest advancements have prompted increasing interests towards multi-LiDAR perception. However, prevailing driving datasets predominantly utilize single-LiDAR systems and collect data devoid of adverse conditions, failing to capture the complexities of real-world environments accurately. Addressing these gaps, we proposed Place3D, a full-cycle pipeline that encompasses LiDAR placement optimization, data generation, and downstream evaluations. Our framework makes three appealing contributions. 1) To identify the most effective configurations for multi-LiDAR systems, we introduce a Surrogate Metric of the Semantic Occupancy Grids (M-SOG) to evaluate LiDAR placement quality. 2) Leveraging the M-SOG metric, we propose a novel optimization strategy to refine multi-LiDAR placements. 3) Centered around the theme of multi-condition multi-LiDAR perception, we collect a 364,000-frame dataset from both clean and adverse conditions. Extensive experiments demonstrate that LiDAR placements optimized using our approach outperform various baselines. We showcase exceptional robustness in both 3D object detection and LiDAR semantic segmentation tasks, under diverse adverse weather and sensor failure conditions. Code and benchmark toolkit are publicly available.

翻译：驾驶感知系统在极端条件下的鲁棒性对于安全关键应用至关重要。最新进展激发了对多LiDAR感知的日益关注。然而，当前主流驾驶数据集主要采用单LiDAR系统，且采集的数据缺乏恶劣条件，未能准确反映真实环境的复杂性。为弥补这些不足，我们提出了Place3D——一个涵盖LiDAR布局优化、数据生成及下游评估的全周期流水线。我们的框架包含三项突出贡献：1) 为识别多LiDAR系统的最佳配置，我们引入语义占据栅格的替代度量（M-SOG）来评估LiDAR布局质量；2) 基于M-SOG度量，我们提出一种新型优化策略以改进多LiDAR布局；3) 以多条件、多LiDAR感知为核心，我们采集了包含清洁与恶劣条件的364,000帧数据集。大量实验表明，采用我们方法优化的LiDAR布局性能优于多种基线。在多样化恶劣天气及传感器故障条件下，该方法在3D目标检测与LiDAR语义分割任务中展现出卓越鲁棒性。代码与基准工具包已公开发布。