ロボットナビゲーションのためのランダム化アルゴリズムによるスケーラブルなネットワーク特徴選択
Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation
未知環境を航行するロボット群の自己位置推定に有用な視覚特徴を、グラフラプラシアンとネットワーク接続性を利用したランダム化アルゴリズムで効率的に選択する手法を提案した。
著者: Vivek Pandey, Arash Amini, Guangyi Liu, Ufuk Topcu, Qiyu Sun, Kostas Daniilidis, Nader Motee
分類: cs.RO
原文アブストラクト
We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, we establish a crucial connection between graph Laplacian and the importance of features. We show that strong network connectivity translates to uniformity in feature importance, which enables uniform random sampling of features and reduces the overall computational complexity. We leverage a scalable randomized algorithm for sparse sums of positive semidefinite matrices to efficiently select the set of the most informative features and significantly improve the probabilistic performance bounds. Finally, we support our findings with extensive simulations.
