Motion Trajectory Sequence-Based Map Matching Assisted Indoor Autonomous Mobile Robot Positioning
Position information is one of basic elements for context awareness of autonomous mobile robots. This paper studies the positioning algorithm of autonomous mobile robots suitable for search and rescue in dark building corridors and underground mine tunnels when an emergency occurs, and proposes a novel map matching aided positioning algorithm based on a Hidden Markov Model. This algorithm does not rely on a camera, and only uses the inertial sensors installed in mobile robot and the indoor map to realize the fusion of dead reckoning and map matching. Firstly, it detects the position-related motion postures during the motion process, and then the motion trajectory is divided into a sub-trajectory sequence. By matching the sub-trajectory sequence with the indoor map, the proposed algorithm achieves tracking and positioning of the mobile robot. In order to verify the effectiveness of the proposed algorithm, this paper adopts four-wheel differentially driven robot to conduct experimental analysis in an actual indoor scenario. The experimental results show that compared with the traditional dead reckoning technology, this algorithm can distinctly reduce the average positioning error of mobile robot, and it is robust to heading angle noises within a certain error range.
KeywordsMobile robot Indoor positioning Hidden Markov Model Posture pattern detection
This work was supported by the National Natural Science Foundation of China (No. 61702288), the Natural Science Foundation of Tianjin in China (No. 16JCQNJC00700) and the Fundamental Research Funds for the Central Universities.
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