融合语义分割的葡萄果园机器人稠密地图构建方法OA

Aiming to address low localization accuracy,unreliable fruit recognition,and poor map quality in vineyard robots,PDS-SLAM,a dense mapping algorithm that integrated semantic segmentation was proposed.Built on ORB-SLAM3,each image was partitioned during feature extraction;the regional FAST threshold was adaptively adjusted according to regional corner counts;and quadtree uniformization method with minimum distance was applied,which improved spatial uniformity and matching robustness of feature points,thereby enhancing localization accuracy.A network,PDSNet,was proposed by integrating a DSA module into PIDNet,enhancing spatial perception of grape clusters and improving fruit recognition.A dense mapping thread and an octree thread were introduced:images were projected to recover local dense point clouds via a point cloud recovery algorithm;statistical outlier filter and radius filter were applied to remove aberrant points;semantic masks were used to annotate grape clusters,yielding a dense semantic map that was finally converted into an octomap.In experiments on the EuRoC dataset and a self-collected dataset,a 27.3％reduction in absolute trajectory error(ATE)on the MH03 sequence relative to ORB-SLAM3 and a 15.5％average increase in matched ORB features were achieved,indicating improved localization accuracy.PDSNet achieved an IoU of 78.9％for grape segmentation at 126.92 f/s.The results demonstrated that PDS-SLAM enhanced localization perception and produced dense semantic maps and octree maps,supporting autonomous navigation and precision operations for orchard robots.