An autonomous positioning method for fire robots with multi-source sensors.

The present technology, based on laser and visual SLAM navigation and positioning, does not apply to the event of a fire in a room where there is a large amount of smoke, for its increasingly obvious defects. In addition, traditional track deduction technology based on photoelectric encoder has accumulated error, and noise disturbance exists in the INS inertial navigation measurement technology, and the UWB positioning technology is vulnerable to NLOS disturbance caused by site occlusion. To solve the problem of accurate positioning of Autonomous Mobile fire-fighting robot in smoke scenes, the system design is optimized by adopting the following methods: using IMU-assisted residual chi-square criterion to detect whether there is NLOS in UWB, introducing IMU instantaneous compensation positioning data and adopting Chan algorithm fitting of the second multiplication to ensure the stability and accuracy of UWB data; meanwhile, a tight combination model of navigation and positioning is designed: via the improved Kalman filter algorithm, fused with the magnetic encoder track estimation pose, UWB absolute and IMU heading angle pose to realize the accurate positioning of the fire robot in the smoke scene. Finally, the fusion simulation model and algorithm are verified by MATLAB, as it shows, the method has an average positioning accuracy of 98.63% in the X-axis direction, 99.52% in the Y-axis direction, and 97.24% in the heading angle, which solves the inherent physical defects of a single positioning sensor and serves as a reliable and accurate solution for indoor robot positioning. [ABSTRACT FROM AUTHOR]