论文标题
用于机器人应用的LIDAR探光仪的自我监督学习
Self-supervised Learning of LiDAR Odometry for Robotic Applications
论文作者
论文摘要
可靠的机器人姿势估计是许多机器人自主管道的关键基础，而LiDAR定位是一个主动的研究领域。在这项工作中，提出了一种多功能的自我监管的激光射击估算方法，以便在维持实时性能的同时有效利用所有可用的激光雷达数据。所提出的方法在训练过程中选择性地采用几何损失，意识到可以从扫描点提取的信息量。此外，不需要标记或地面真实数据，因此，在难以获得准确的地面真相的应用中，提出的方法适用于姿势估计。此外，提出的网络体系结构适用于广泛的环境和传感器模式，而无需任何网络或损失函数调整。通过使用腿部，跟踪和车轮机器人进行的多种实验，对室内和室外现实世界应用进行了彻底测试所提出的方法，这证明了基于学习的激光镜的适用性对于复杂的机器人应用。
Reliable robot pose estimation is a key building block of many robot autonomy pipelines, with LiDAR localization being an active research domain. In this work, a versatile self-supervised LiDAR odometry estimation method is presented, in order to enable the efficient utilization of all available LiDAR data while maintaining real-time performance. The proposed approach selectively applies geometric losses during training, being cognizant of the amount of information that can be extracted from scan points. In addition, no labeled or ground-truth data is required, hence making the presented approach suitable for pose estimation in applications where accurate ground-truth is difficult to obtain. Furthermore, the presented network architecture is applicable to a wide range of environments and sensor modalities without requiring any network or loss function adjustments. The proposed approach is thoroughly tested for both indoor and outdoor real-world applications through a variety of experiments using legged, tracked and wheeled robots, demonstrating the suitability of learning-based LiDAR odometry for complex robotic applications.
