Active SLAM over Continuous Trajectory and Control: A Covariance-Feedback Approach
- Resource Type
- Conference
- Authors
- Koga, Shumon; Asgharivaskasi, Arash; Atanasov, Nikolay
- Source
- 2022 American Control Conference (ACC) Control Conference (ACC), 2022 American. :5062-5068 Jun, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Simultaneous localization and mapping
Uncertainty
Robot kinematics
Stochastic processes
Optimal control
Entropy
Sensors
- Language
- ISSN
- 2378-5861
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.