UWB/LiDAR Tightly Coupled Positioning Algorithm Based on ISSA Optimized Particle Filter

• Published in: IEEE sensors journal Vol. 24; no. 7; pp. 11217 - 11228
• Main Authors: Wang, Xuyin, Gao, Fangzheng, Huang, Jiacai, Xue, Yuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Base stations; Data integration; Errors; Extended Kalman filter; Graph optimization; improved sparrow search algorithm (ISSA); indoor position; Kalman filters; Laser radar; Lidar; Nonlinear optics; particle filter; Particle filters; Particle measurements; Search algorithms; Sensors; Ultrawideband
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3366941

To deal with the disadvantages of non-line-of-sight (NLOS) errors in ultrawideband (UWB) and cumulative errors in LiDAR which impact positioning accuracy, a UWB/LiDAR tightly coupled positioning method is presented in this article by the improved sparrow search algorithm (ISSA) optimized particle filter. By incorporating LiDAR measurements, this method offers the distance estimation between the combined positioning system and the UWB base station and eliminates the NLOS errors in the UWB measurement value. In addition, the ISSA is used to eliminate the particle degradation phenomenon of particle filter and reduce the required number of particles, which significantly enhances the speed and real-time performance of the data fusion algorithm. Finally, the combined function of UWB/LiDAR is constructed to optimize the global position based on the graph optimization method. The experimental results demonstrate that the particle filter algorithm optimized by ISSA achieves comparable results while using only 25% of the particles required by the original particle filter algorithm. Moreover, the proposed method improves the positioning accuracy by 69.16% and 59.63% compared with UWB and LiDAR alone, and it improves the positioning accuracy by 55.71% compared with fusion positioning using extended Kalman filter (EKF). These results highlight the effectiveness of the proposed method in achieving accurate positioning.