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 $^2$ .

Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position.