Abstract: Autonomous navigation has been widely used in complex orchard environments. Global Navigation Satellite System signals can be obtained after navigation. Specifically, Real-Time Kinematic solutions are frequently interrupted by dense canopy occlusion, leading to signal loss. However, the conventional laser Simultaneous Localization and Mapping can often suffer from substantial drift, due to the discrete, unstable geometric features and high local environmental similarity. Particularly, the feature extraction can rely heavily on the ground or planar structures. Such features are often absent, ambiguous, or highly repetitive in agricultural settings. The reliable localization and mapping are often required for the intelligent spraying agricultural robots in precise positioning tasks. In this study, a robust and high-precision framework was proposed for LiDAR-inertial positioning and mapping. The stable features of the tree trunk were also extracted within the arboreal environment. Thereby, the more robust observational constraints were added to the Kalman filter. Specifically, the Euclidean range was employed to effectively filter out the distant background noise. Distance smoothness criteria were used for the spatial continuity of the point segments. The concavity-convexity consistency was evaluated to verify the external shape of the trunks. The curvature regularity was then assessed for the properties of the cylindrical geometry. A series of tests was carried out to calibrate the experimental parameter. The valid features were effectively distinguished among inclined trunks in the quasi-dynamic orchard. Unstable features were removed to thereby reinforce the geometric constraints in the frame-to-frame registration. A tree loop descriptor was then introduced to optimize the global consistency on the back end. The spatial distribution of the trees was obtained for their topological relationships. Geometric triangle descriptors were constructed using inter-tree distances and rotation-invariant radial binary descriptors. Effective loop closures were identified and then registered using a dual-distance screening mechanism. The reliable loop detection was realized to eliminate the cumulative errors. A series of experiments was also conducted on both the publicly available BotanicGarden dataset and a self-collected nursery dataset. The quantitative analysis was utilized to take the Root Mean Square Error of the Absolute Pose Error as the primary metric, where the high-precision differential signals served as the ground truth trajectory. The results demonstrate that the better performance significantly outperformed the current state-of-the-art algorithms in complex orchard environments. Specifically, the trajectories also exhibited a high degree of overlap with the ground-truth paths on the BotanicGarden dataset. Statistical analysis indicated that the diverse challenging environments were also optimized, including the dense tree scenarios, riverside areas with sparse vegetation, and mixed tree-lawn zones. Consequently, the localization accuracy was improved by approximately 34.7% and 46.9%, respectively, compared with the widely-used FAST-LIO2 and the FAST-LIO-SAM algorithm. The self-collected dataset was utilized to specifically design for the different scenarios. Five scenarios were represented by different vegetation types and spatial structures, including a mixed planting area of Red Maple and Crape Myrtle, a complex mixed zone of Cactus Balls and Chinese Rose trees, a densely vegetated area of Begonia trees, a sparse potted Sweet Osmanthus section, and a high-density Cactus Ball planting zone. Stable trunk features were extracted to provide the robust observational constraints. While the tree-based descriptor was screened for the loop closure detection, thereby significantly enhancing the localization precision. As a result, the high accuracy was then improved by approximately 42.4% over FAST-LIO2 and 48.3% over SC-LIO-SAM. The robot paths were estimated to be closely aligned with the high-precision ground-truth references, with no significant cumulative drift during long-term operation. In conclusion, the stable trunk geometric features were fused with a topology-aware loop closure. The robustness and accuracy of the localization were significantly enhanced under complex orchard environments in precision agriculture.