Active Exploration and Mapping via Iterative Covariance Regulation over Continuous $SE(3)$ Trajectories
This addresses the challenge of efficient mapping for mobile robots, but it appears incremental as it builds on existing optimal control and covariance minimization methods.
The paper tackled the problem of active exploration and mapping for mobile robots by developing iterative Covariance Regulation (iCR) to minimize map uncertainty through optimal control over SE(3) trajectories, demonstrating autonomous exploration and uncertainty reduction in simulated occupancy grid environments.
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.