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.

翻译：激光雷达传感器是三维感知的关键模态，但其设计通常独立于下游任务（如点云配准）。传统配准方法在预先采集的数据集上运行，采用固定的激光雷达配置，导致数据采集欠优，且在采样、噪声滤波和参数调优方面产生显著计算开销。本研究提出一种自适应激光雷达感知框架，能够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。通过将配准反馈集成至感知闭环，我们的方法在点云密度、噪声与稀疏性之间实现最优平衡，从而提升配准精度与效率。在CARLA仿真环境中的评估表明，该方法在保持泛化能力的同时，性能优于固定参数基线，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。