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

2021/03/10 by Shumon Koga, Koga, Shumon, Arash Asgharivaskasi +3 · 2 citations
Computer Science · Engineering · #Advanced Vision and Imaging #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.2103.05819

openalex publication_date 2021/03/10 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28

Abstract

This paper develops 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.

Cited by

• B-ActiveSEAL: Scalable Uncertainty-Aware Active Exploration with Tightly Coupled Localization-Mapping
• Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation

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