A real time LiDAR-Visual-Inertial object level semantic SLAM for forest environments

The accurate positioning of individual trees, the reconstruction of forest environment in three dimensions and the identification of tree species distribution are crucial aspects of forestry remote sensing. Simultaneous Localization and Mapping (SLAM) algorithms, primarily based on LiDAR or visual technologies, serve as essential tools for outdoor spatial positioning and mapping, overcoming signal loss challenges caused by tree canopy obstruction in the Global Navigation Satellite System (GNSS). To address these challenges, a semantic SLAM algorithm called LVI-ObjSemantic is proposed, which integrates visual, LiDAR, IMU and deep learning at the object level. LVI-ObjSemantic is capable of performing individual tree segmentation, localization and tree spices discrimination tasks in forest environment. The proposed Cluster-Block-single and Cluster-Block-global data structures combined with the deep learning model can effectively reduce the cases of misdetection and false detection. Due to the lack of publicly available forest datasets, we chose to validate the proposed algorithm on eight experimental plots. The experimental results indicate that the average root mean square error (RMSE) of the trajectories across the eight plots is 2.7, 2.8, 1.9 and 2.2 times lower than that of LIO-SAM, FAST-LIO2, LVI-SAM and FAST-LIVO, respectively. Additionally, the mean absolute error in tree localization is 0.12 m. Moreover, the mapping drift of the proposed algorithm is consistently lower than that of the aforementioned comparison algorithms.