連続SE(3)軌道上での反復共分散制御による能動的探索とマッピング
Active Exploration and Mapping via Iterative Covariance Regulation over Continuous $SE(3)$ Trajectories
移動ロボットのSE(3)姿勢運動を最適制御し、地図推定の共分散を反復的に最小化することで能動的探索とマッピングを行う手法を提案した。
著者: Shumon Koga, Arash Asgharivaskasi, Nikolay Atanasov
分類: cs.RO, math.OC
原文アブストラクト
This paper develops \emph{iterative Covariance Regulation} (iCR), a novel method for active exploration and mapping for a mobile robot equipped with on-board sensors. The problem is posed as optimal control over the $SE(3)$ pose kinematics of the robot to minimize the differential entropy of the map conditioned the potential sensor observations. We introduce a differentiable field of view formulation, and derive iCR via the gradient descent method to iteratively update an open-loop control sequence in continuous space so that the covariance of the map estimate is minimized. We demonstrate autonomous exploration and uncertainty reduction in simulated occupancy grid environments.
