Active Exploration and Mapping via Iterative Covariance Regulation over Continuous $SE(3)$ Trajectories

TL;DR

This paper introduces iterative Covariance Regulation (iCR), a novel optimal control method for active exploration and mapping of robots in continuous $SE(3)$ space, focusing on minimizing map uncertainty.

Contribution

The paper presents a new iterative control algorithm for active exploration that optimizes robot trajectories to reduce map uncertainty in continuous space.

Findings

Effective in simulated occupancy grid environments

Reduces map covariance through iterative control updates

Demonstrates autonomous exploration capabilities

Abstract

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.