论文标题
底端优化3D对象检测和本地化的激光束束配置
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
论文作者
论文摘要
现有的基于LIDAR应用的学习方法使用在预定的梁配置下扫描的3D点，例如，梁的高度角度通常均匀分布。那些固定的配置是任务不合时宜的，因此简单地使用它们可以导致次优性能。在这项工作中，我们采取一条新路线来学习优化给定应用程序的激光束配置。具体而言，我们建议基于增强学习的学习对优化（RL-L2O）框架，以自动以基于激光雷达的应用程序的端到端方式以端到端的方式优化光束配置。优化以目标任务的最终性能为指导，因此我们的方法可以轻松地与任何基于激光雷达的应用程序作为简单的倒入模块集成。当需要低分辨率（低成本）激光雷达以进行大规模部署时，该方法特别有用。我们使用我们的方法来搜索低分辨率激光痛的光束配置，以完成两个重要任务：3D对象检测和定位。实验表明，与基线方法相比，提出的RL-L2O方法可显着提高这两个任务的性能。我们认为，我们的方法与最近可编程激光痛的最新进展的结合可以为基于激光雷达的主动感知启动新的研究方向。该代码可在https://github.com/vniclas/lidar_beam_selection上公开获取
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
