Application and Improvement of Multisensor Fasion SLAM Algorithm in Forestry Intelligence
The Simultaneous Localization and Mapping(SLAM)algorithm enables robots to locate themselves in unknown envi-ronments and to build incremental maps of their surroundings,forming the foundation for autonomous navigation.Due to is-sues such as low localization accuracy and poor map consistency associated with SLAM algorithms that rely on single sen-sors(such as GNSS,LiDAR,and cameras)in forest environments,a multi-sensor fusion SLAM algorithm,referred to as RS-LVI-SAM,is proposed.First,this approach combines LiDAR,a monocular camera,and inertial navigation to con-struct a 3D point cloud map of the forest environment while simultaneously providing real-time localization for forestry ro-bots.Second,the RS-T loop closure frame search method,which is time-constrained,is introduced to perform candidate loop closure frame searches.The results showed that,for the M2DGR dataset,the RS-LVI-SAM algorithm achieves a root mean square error of 0.09 m and a standard deviation of 0.04 m for the generated trajectory,demonstrating the highest lo-calization accuracy compared to current classical SLAM algorithms.In a forest scenario,the chest diameter of Platycladus orientalis in the point cloud map constructed by the RS-LVI-SAM algorithm shows an root-mean-square error and absolute mean error of 1.27 cm and 1.16 cm,respectively,indicating optimal mapping performance.Furthermore,the root-mean-square error and absolute mean error between the generated trajectory and the ground truth trajectory are 0.46 m and 0.34 m,respectively,reflecting the highest localization accuracy.Therefore,the RS-LVI-SAM algorithm enables precise local-ization and forest point cloud map construction in forest environments,providing technological support for robotic operations in forestry.
Forestry robotSimultaneous localization and mappingSensorLaser radar
万方数据
维普
