オドメトリとスパース距離サンプリングを組み合わせた動的屋内環境における生涯位置推定
Lifelong Localization in Dynamic Indoor Environments Combining Odometry with Sparse Distance Sampling
動的な屋内環境で、オドメトリと少数の距離サンプルを用いてロボットの位置を頑健に推定する生涯位置推定フレームワークを提案した。
詳しい要約
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著者: Michael M. Bilevich, Tomer Buber, Dan Halperin
分類: cs.RO
原文アブストラクト
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.
