Efficient active SLAM based on submap joining

Publication Type:
Conference Proceeding
Citation:
Australasian Conference on Robotics and Automation, ACRA, 2017, 2017-December pp. 141 - 147
Issue Date:
2017-01-01
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This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy.
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