Abstract
This paper investigates the multi-sensor fusion positioning problem coal mine rescue robots to deal with the complex catastrophic underground environment subject to poor illumination and slippery road. A multi-sensor fusion positioning system is designed by combining lidar, IMU, and wheel encoder and a multi-sensor fusion positioning method is proposed based on extended Kalman filter with fuzzy confidence. By the proposed method, the wheel slip can be described and used to regulate the confidence level of the wheel odometry information in the fusion positioning system in real-time, and change the covariance matrix dynamically to improve the adaptability of the system to the subterranean environment and enhance the positioning accuracy. The experimental results show that the system is suitable for underground positioning, and the proposed positioning algorithm is of higher accuracy than the conventional single sensor positioning methods and the extended Kalman filter fusion positioning methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
