The reliability of driving perception systems under unprecedented conditions is crucial for practical usage. Latest advancements have prompted increasing interest in 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 the 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 280,000-frame dataset from both clean and adverse conditions. Extensive experiments demonstrate that LiDAR placements optimized using our approach outperform various baselines. We showcase exceptional results in both LiDAR semantic segmentation and 3D object detection tasks, under diverse weather and sensor failure conditions.

翻译：驾驶感知系统在极端条件下的可靠性对其实际应用至关重要。最新进展促使人们对多激光雷达感知的兴趣日益增长。然而，主流驾驶数据集主要采用单激光雷达系统，且采集的数据缺乏恶劣条件，未能准确捕捉真实环境的复杂性。为弥补这些不足，我们提出了Place3D——一个涵盖激光雷达布局优化、数据生成与下游评估的全周期流程。我们的框架具有三个突出贡献：1）为确定多激光雷达系统的最有效配置，我们引入语义占据网格的代理度量（M-SOG）来评估激光雷达布局质量；2）利用M-SOG度量，我们提出一种新颖的优化策略以改进多激光雷达布局；3）围绕多条件多激光雷达感知主题，我们采集了包含清洁与恶劣条件的28万帧数据集。大量实验表明，采用我们方法优化的激光雷达布局优于各类基线方案。我们在激光雷达语义分割和3D目标检测任务中均展示了卓越性能，这些实验覆盖了多种天气条件与传感器故障场景。