Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distributed. Those fixed configurations are task-agnostic, so simply using them can lead to sub-optimal performance. In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application. Specifically, we propose a reinforcement learning-based learning-to-optimize (RL-L2O) framework to automatically optimize the beam configuration in an end-to-end manner for different LiDAR-based applications. The optimization is guided by the final performance of the target task and thus our method can be integrated easily with any LiDAR-based application as a simple drop-in module. The method is especially useful when a low-resolution (low-cost) LiDAR is needed, for instance, for system deployment at a massive scale. We use our method to search for the beam configuration of a low-resolution LiDAR for two important tasks: 3D object detection and localization. Experiments show that the proposed RL-L2O method improves the performance in both tasks significantly compared to the baseline methods. We believe that a combination of our method with the recent advances of programmable LiDARs can start a new research direction for LiDAR-based active perception. The code is publicly available at https://github.com/vniclas/lidar_beam_selection

翻译：现有基于激光雷达的学习方法通常使用预定义波束配置下扫描的3D点，例如波束俯仰角往往均匀分布。这些固定配置与具体任务无关，直接使用会导致性能欠优。本文另辟蹊径，提出针对特定应用优化激光雷达波束配置的学习方法。具体而言，我们提出基于强化学习的"学习优化"框架（RL-L2O），以端到端方式自动优化不同激光雷达应用的波束配置。该优化由目标任务最终性能引导，因此可作为简易即插即用模块轻松集成至任意激光雷达应用系统。本方法在需要低分辨率（低成本）激光雷达的场景下尤为实用，例如大规模系统部署。我们将该方法应用于低分辨率激光雷达的波束配置搜索，以完成两项重要任务：3D目标检测与定位。实验表明，与基线方法相比，所提出的RL-L2O方法显著提升了这两项任务的性能。我们认为，本方法与可编程激光雷达的最新进展相结合，将开辟基于激光雷达主动感知的新研究方向。相关代码已开源发布于 https://github.com/vniclas/lidar_beam_selection