A curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields

Abstract

Due to the undulating topography, the world contains numerous irregularly shaped small fields. Autonomous navigation along the curved edges of these fields is crucial for achieving complete unmanned operation in these small-field environments. Based on this, we proposed an accurate and robust curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields. Leveraging the significant height difference between headland and farmland, we employed an efficient RGB-D multimodal semantic segmentation network to achieve high-precision segmentation of headland area. Meanwhile, a slope-based feature points adaptive selection algorithm and a feature points transformation algorithm fusing multimodal depth data were proposed innovatively for curved navigation path extraction. By utilizing the RGB-D segmentation model ESANet, we achieved a mean intersection over union (mIoU) of 96.94 %, surpassing other RGB segmentation models. The mean deviations between the ground truth path and the extracted path were less than 6.43 pixels on an image with a resolution of 640 × 480. The proposed method can finally achieve a processing efficiency of 23.80 FPS on images in 640 × 480 resolution, and the field experiments tested that the mean deviations of the agricultural vehicle navigating along the extracted curved navigation path were less than 0.188 m, which met the real-time performance requirement and accuracy requirement of autonomous navigation.