Lifelong Localization in Dynamic Indoor Environments Combining Odometry with Sparse Distance Sampling
2026-07-20 • Robotics
Robotics
AI summaryⓘ
The authors developed a way for robots to figure out where they are inside buildings, even when things around them move unexpectedly. They use simple distance measurements combined with the robot's own movement data to keep track of its location. Their method can quickly fix the robot's position if it suddenly gets moved somewhere else, and it works well even when obstacles change, as long as the robot has learned about those changes. This approach works almost as well as methods using full laser scans but only needs a few distance points, making it cheaper and easier to use.
robot localizationodometrydistance samplingkidnapped robot problemdynamic environmentsSLAMindoor navigationsensor costpose estimation
Authors
Michael M. Bilevich, Tomer Buber, Dan Halperin
Abstract
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.