A Forest 3-D Lidar SLAM System for Rubber-Tapping Robot Based on Trunk Center Atlas

Currently, there are many simultaneous localization and mapping (SLAM) algorithms for 3-D light detection and ranging (lidar) relying on features such as planes and edges. However, if a robot needs to work in a rubber forest, these simple features can be unstable due to the complexity of the environment. To address this problem, in this article, we propose the first SLAM system to make a deep adaptive adjustment for the particularity of the rubber-tapping robot and the rubber forest environment. In our article, tree trunks are used to construct a set of sparse maps called trunk atlas, which contain environmental information for localization and trunk position for rubber tapping. To ensure the quality of feature extraction, density field correction and multicriteria trunk detection are proposed. Due to the sparsity and stability of the trunk atlas, our SLAM system can ensure real-time performance. Experiments show that our lidar-only SLAM system can achieve nearly the same performance as state-of-the-art inertial-measurement-unit-aided algorithms in terms of runtime and localization accuracy. In addition, our trunk atlas also takes up less memory for storage than other algorithms. More specifically, it takes less than 50 kB to store a map of an area of approximately 5000 m$^2$.