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Addressing the issues with existing orchard navigation methods, which are susceptible to canopy density, lighting conditions, irregular planting, and uneven ground, leading to low stability in tree row direction estimation and row end identification methods used for autonomous navigation, a 3D point cloud-based method for pathway planning was introduced. The method comprised three primary components, a tree row identification technique, a scene recognition method, and a pathway planning strategy. These formed a system that enabled robots to autonomously shuttle between densely planted rows. Initially, a trunk point cloud extraction method using a semantic segmentation network was developed for tree row identification and positioning. Subsequently, a convolutional neural network-based method was established for location scene recognition to identify various scenarios within rows. Finally, an inter-row movement strategy managed by a finite state machine and a pathway planning method based on RS curves were designed for continuous walking through multiple rows. The trunk point cloud segmentation achieved an average IoU of 88.3%, with tree localization errors of 2.04% in the x-direction and 1.54% in the y-direction. The average error in tree row direction estimation was 1.11°.The row-end recognition accuracy reached 96%,and the average deviation of in-line centerline walking was 0.08m. These results showed that the proposed methods met the accuracy requirements for tree row positioning and scene recognition in outdoor orchards, which effectively planed pathways between rows and served as a reliable reference for autonomous laser-guided navigation.