LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening in self-symmetric surroundings such as tunnels. This work addresses precisely this issue by proposing a neural network-based estimation approach for detecting (non-)localizability during robot operation. Special attention is given to the localizability of scan-to-scan registration, as it is a crucial component in many LiDAR odometry estimation pipelines. In contrast to previous, mostly traditional detection approaches, the proposed method enables early detection of failure by estimating the localizability on raw sensor measurements without evaluating the underlying registration optimization. Moreover, previous approaches remain limited in their ability to generalize across environments and sensor types, as heuristic-tuning of degeneracy detection thresholds is required. The proposed approach avoids this problem by learning from a collection of different environments, allowing the network to function over various scenarios. Furthermore, the network is trained exclusively on simulated data, avoiding arduous data collection in challenging and degenerate, often hard-to-access, environments. The presented method is tested during field experiments conducted across challenging environments and on two different sensor types without any modifications. The observed detection performance is on par with state-of-the-art methods after environment-specific threshold tuning.
翻译:以LiDAR为基础的本地化和绘图是许多现代机器人系统的核心组成部分之一,原因是将射程和几何方法直接结合起来,从而可以精确地估计运动和实时生成高质量的地图。然而,由于现场的环境限制不足,这种对几何方法的依赖可能导致本地化失败,发生在隧道等自我对称环境中。这项工作通过提出基于神经网络的估算方法来解决这一问题,以便在机器人操作期间探测(非)本地化。特别注意扫描到扫描的登记是否适合本地化,因为它是许多LiDARodology估计管道的一个关键组成部分。与以往相比,多数是传统的探测方法,对几何方法的依赖可能会导致本地化失败,导致在不评估基本的注册优化的情况下,在原始传感器测量的本地化环境中发生。此外,以往的方法仍然有限,因为需要对机率检测阈值的临界值进行超常性调整,因此拟议的方法避免了这一问题,因为从不同环境的收集中学习,允许平面网络在各种实地的实地测试中完全通过具有挑战性能的模型进行。