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.

中文翻译：

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用于森林环境的实时 LiDAR 视觉惯性对象级语义 SLAM


单棵树的准确定位、森林环境的三维重建和树种分布的识别是林业遥感的关键方面。同步定位和地图构建 （SLAM） 算法主要基于 LiDAR 或视觉技术，是户外空间定位和地图构建的重要工具，克服了全球导航卫星系统 （GNSS） 中树冠遮挡造成的信号丢失挑战。为了应对这些挑战，提出了一种名为 LVI-ObjSemantic 的语义 SLAM 算法，该算法在对象层面集成了视觉、LiDAR、IMU 和深度学习。LVI-ObjSemantic 能够在森林环境中执行单个树木分割、定位和树木香料鉴别任务。所提出的 Cluster-Block-single 和 Cluster-Block-global 数据结构结合深度学习模型，可以有效减少误检和误检的情况。由于缺乏公开可用的森林数据集，我们选择在 8 个实验样地上验证所提出的算法。实验结果表明，8 个样地轨迹的平均均方根误差 （RMSE） 分别比 LIO-SAM 、 FAST-LIO2 、 lvi-SAM 和 FAST-LIVO 低 2.7 、 2.8 、 1.9 和 2.2 倍。此外，树定位的平均绝对误差为 0.12 m。此外，所提算法的映射漂移始终低于上述比较算法的映射漂移。