Active SLAM over Continuous Trajectory and Control: A Covariance-Feedback Approach
This work addresses robot navigation and mapping challenges in environments with limited sensing, offering incremental improvements in trajectory optimization for SLAM.
The paper tackles active SLAM by optimizing continuous robot trajectories to minimize landmark uncertainty, using a stochastic optimal control approach that combines open-loop and closed-loop methods to improve sensing efficiency.
This paper proposes a novel active Simultaneous Localization and Mapping (SLAM) method with continuous trajectory optimization over a stochastic robot dynamics model. The problem is formalized as a stochastic optimal control over the continuous robot kinematic model to minimize a cost function that involves the covariance matrix of the landmark states. We tackle the problem by separately obtaining an open-loop control sequence subject to deterministic dynamics by iterative Covariance Regulation (iCR) and a closed-loop feedback control under stochastic robot and covariance dynamics by Linear Quadratic Regulator (LQR). The proposed optimization method captures the coupling between localization and mapping in predicting uncertainty evolution and synthesizes highly informative sensing trajectories. We demonstrate its performance in active landmark-based SLAM using relative-position measurements with a limited field of view.