分散型視覚慣性協調自己位置推定
Distributed Visual-Inertial Cooperative Localization
複数ロボットが環境特徴とループ閉じ込み制約を共有し、共分散交差を用いて未知の相関を補償しながら分散的に自己位置を推定する手法を提案した論文。
著者: Pengxiang Zhu, Patrick Geneva, Wei Ren, Guoquan Huang
分類: cs.RO, cs.CV
原文アブストラクト
In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure constraints across time and robots. In particular, we leverage covariance intersection (CI) to allow each robot to only estimate its own state and autocovariance and compensate for the unknown correlations between robots. Two novel multi-robot methods for utilizing common environmental SLAM features are introduced and evaluated in terms of accuracy and efficiency. Moreover, we adapt CI to enable drift-free estimation through the use of loop-closure measurement constraints to other robots' historical poses without a significant increase in computational cost. The proposed distributed CL estimator is validated against its non-realtime centralized counterpart extensively in both simulations and real-world experiments.
