To address issues such as leaf occlusion, misalignment of the harvesting robotic arm, and limited harvesting range in hillside chili fields, this paper designs an intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling. The system integrates real-time data from a LiDAR and IMU inertial navigation system to reconstruct the chili point cloud occluded by leaves from multiple perspectives. To address issues such as misalignment of the robotic arm caused by terrain undulations, the system integrates an adaptive leveling platform and an H-shaped planar slide, combined with a gyroscope to dynamically adjust the arm’s posture in real time, ensuring arm stability while expanding its workspace. In addition, to ensure harvesting efficiency and pepper integrity, an integrated cutting–gripping flexible end effector is designed to achieve synchronized cutting and collection operations. The experiment shows that the system achieves recognition accuracy of 81.95% for occluded chili peppers and 89.04% for non-occluded chili peppers. The harvesting success rate is 86.33%, with a single harvesting operation taking 13.17 s. During prolonged operation, the harvesting success rate can be maintained at approximately 85.1%. In summary, the intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling provides a feasible solution for automated pepper harvesting.

Accurate 3D semantic perception is essential for robotic operations in multi-tier caged-pigeon houses, where dense cage-mounted facilities, repetitive layouts, local geometric similarity, and dynamic objects make static-facility segmentation difficult. To address these issues, this study proposes GSR-PointNet++, a point cloud semantic segmentation method with dynamic suppression. A Hierarchical Dynamic Suppression Front-End (HDS-Front) is introduced before static map construction to reduce dynamic interference caused by pigeon activity and human movement. Based on the processed static point clouds, a semantic segmentation dataset for multi-tier caged-pigeon houses is constructed. GSR-PointNet++ is developed on the PointNet++ backbone and incorporates a Geometric Structure-Relationship Module (GSRM), which models local normal consistency and spatial relationships to enhance feature representation in structurally repetitive scenes. Geometry-aware Prototype Consistency Learning (GPCL) is further introduced during training to improve class separability and prediction stability. Independent testing on the annotated dataset collected from the second pigeon house showed that the proposed method achieved a mean intersection over union (mIoU) of 96.29% ± 0.25% and an overall accuracy (OA) of 98.62% ± 0.13% for the four target facility classes, outperforming PointNet++ by 5.92 and 2.54 percentage points, respectively. These results indicate that the proposed method improves point cloud semantic segmentation for static facilities and has potential to support robotic perception in structured livestock environments.