SLAM Mapping Method of Laser Radar for Tobacco Production Line Inspection Robot Based on Improved RBPF

The study focuses on the laser radar SLAM mapping method employed by the tobacco production line inspection robot, utilizing an enhanced RBPF approach. It involves the construction of a well-structured two-dimensional map of the inspection environment for the tobacco production line inspection robot. This construction aims to ensure the seamless execution of inspection tasks along the tobacco production line. The fusion of wheel odometer and IMU data is accomplished using the extended Kalman filter algorithm, wherein the resulting fused odometer motion model and LiDAR observation model jointly serve as the hybrid proposal distribution. In the hybrid proposal distribution, the iterative nearest point method is used to find the sampling particles in the high probability area, and the matching score during particle matching scanning is used as the fitness value, and the Drosophila optimization strategy is used to adjust the particle distribution. Then, the weight of each particle after optimization is solved, and the particles are adaptively resampled according to the size of the weight after solution, and the inspection map of the inspection robot of the tobacco production line is updated according to the updated position and posture information and observation information of the particles of the inspection robot of the tobacco production line. The experimental results show that this method can realize the laser radar SLAM mapping of the tobacco production line inspection robot, and it can build a more ideal two-dimensional map of the inspection environment of the tobacco production line inspection robot with fewer particles. If it is applied to practical work, a more ideal work effect can be achieved.

@article{Liang2023,
  title     = {SLAM Mapping Method of Laser Radar for Tobacco Production Line Inspection Robot Based on Improved RBPF},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Zhiyuan Liang and Pengtao He and Wenbin Liang and Xiaolei Zhao and Bin Wei},
  doi       = {10.14569/IJACSA.2023.0140894},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140894}
}