Navigation Line Extraction Method for Alfalfa Crops Based on RACG-RandLA Point Cloud Segmentation Model

Early-stage alfalfa navigation faces challenges like low plants, narrow rows, and weed interference, causing camera–LiDAR colored point clouds to suffer from sparsity, discontinuous boundaries, and varying illumination. Standard point-level semantic segmentation struggles to support stable crop row allocation and navigation line fitting under these conditions. To address this, we propose RACG-RandLA, a multi-output row-aware color-geometric point cloud segmentation model. Pseudo-labels (crop, background, ‘ignore’) are generated using color and spatial priors, alongside transverse offset and point-level confidence labels for crop points. Built on RandLA-Net, the multi-task network simultaneously outputs crop semantics, transverse offsets, and confidences. It features a color-geometry residual fusion module that adaptively integrates 3D geometric and RGB/ExG features via zero-initialized scaling to handle complex lighting and missing data. Additionally, a late Row-cued Local Feature Aggregation (LFA) module embeds longitudinal continuity and transverse offset constraints into deep layers, effectively mitigating cross-row feature aliasing. During inference, a multi-output pipeline utilizes these predictions for reliable crop point filtering, center refinement, and navigation line fitting. Experiments show that the xyzrgb_exg input achieves an optimal balance between accuracy and conciseness. RACG-RandLA achieves a Test IoU of 0.8590, outperforming PointNet variants, and secures the highest Row Count Accuracy of 0.8761. Furthermore, it reduces lateral navigation jitter to 0.0241 m while maintaining an 85.04% success rate. Ultimately, the proposed method demonstrates a superior balance of semantic accuracy, structural consistency, and navigation stability, providing a robust perception solution for agricultural robots.