让激光雷达自动调整参数，提升点云配准精度与效率
End-to-End LiDAR optimization for 3D point cloud registration
- 将点云配准反馈引入传感环节，动态优化激光雷达参数
- 在CARLA仿真中显著优于固定参数方法，配准更准更快
- 适合自动驾驶和机器人感知中的自适应传感器设计
激光雷达是三维感知的关键模态，但其通常独立于下游任务（如点云配准）进行设计。传统配准方法基于预采集数据集与固定激光雷达配置，导致数据采集不最优，并带来采样、去噪及参数调优的显著计算开销。本文提出一种自适应激光雷达感知框架，可动态调整传感器参数，联合优化激光雷达采集与配准超参数。通过将配准反馈融入感知环路，该方法在点密度、噪声与稀疏性之间实现最优平衡，显著提升配准精度与效率。在CARLA仿真环境下的评估表明，该方法优于固定参数基线，同时保持良好的泛化能力，凸显了自适应激光雷达在自主感知与机器人应用中的潜力。
原文摘要 · Abstract (English)
LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR configurations, leading to suboptimal data collection and significant computational overhead for sampling, noise filtering, and parameter tuning. In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters, jointly optimizing LiDAR acquisition and registration hyperparameters. By integrating registration feedback into the sensing loop, our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency. Evaluations in the CARLA simulation demonstrate that our method outperforms fixed-parameter baselines while retaining generalization abilities, highlighting the potential of adaptive LiDAR for autonomous perception and robotic applications.
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