发表机构
Blavatnik School of Computer Science and Artificial Intelligence, Tel-Aviv University（特拉维夫大学布拉瓦尼克计算机科学与人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究动态室内环境中机器人定位问题，提出结合里程计与稀疏距离采样的终身定位框架，能实时解决绑架机器人问题，在静态和动态环境中均收敛到真实姿态，少量距离样本即可实现与SLAM相当的定位，具多方面优势。
AI 中文摘要
定位是机器人导航中的关键任务，现有多种技术。在许多场景中，机器人会面临不可预见的动态障碍物，使预定地图定位不准确。本文提出一个在动态平面室内环境中强大的终身定位框架，利用机器人里程计和稀疏距离采样。展示了距离样本如何为机器人位置提供强大先验，能实时解决绑架机器人问题。基于实际记录数据考虑动态障碍物，将先验与里程计融合以收敛到机器人位置。该方法在静态和动态环境中都能证明收敛到机器人真实姿态，仅用少量距离样本就能实现与SLAM相当的定位，在传感器成本等方面具有优势。
英文摘要
Localization is a key task in robot navigation, and many techniques exist for it. In many plausible scenarios, a robot might face unforeseen, dynamic obstacles, rendering any pre-determined map inaccurate for localization. In this work, we propose a robust lifelong localization framework in dynamic planar indoor environments, using the robot's odometry and sparse distance sampling. We demonstrate how distance samples can be used to provide a robust prior on the robot's location. This technique can solve the kidnapped robot problem in real time, up to symmetries. Based on insights from real-world recorded data, we also account for dynamic obstacles. We then fuse this prior, over time, with the odometry to converge to the robot's location. A central property of our method is that it provably converges to the robot's ground truth pose even in large indoor environments when the environment is static. We further show that this guarantee also holds in dynamic environments, as long as the nature of those changes has been correctly learned. We demonstrate the effectiveness of our approach in different real-world indoor environments. In particular, we achieve a localization comparable to SLAM with merely a few (sixteen) distance samples, as opposed to the full LiDAR range. Sufficing with only sparse distance sampling is advantageous in terms of sensor cost, privacy, storage space, and transmission bandwidth.
CommentsAppeared in the 2026 IEEE International Conference on Robotics and Automation (ICRA)
