A Deep Learning-Driven Binocular Vision Path Detection Approach for Orchard Robots

Abstract

Autonomous driving relying on visual navigation plays a vital role in promoting automation within the jujube industry. Conventional visual navigation strategies fail to satisfy the demands of straddle-type jujube harvesters owing to their unique row-straddling configuration and complex orchard environments. Accordingly, this paper proposes a novel binocular vision-based path detection algorithm for autonomous jujube harvesters. In the proposed method, target trunks detected from binocular images are used to generate a navigation path that better aligns with the operating trajectory of harvester. A Single Shot MultiBox Detector (SSD) deep learning model is employed to detect trunk bounding boxes. To mitigate interference induced by false detections from the deep learning model, a curve-fitting-based path calibration strategy is implemented. Experimental results demonstrate that the proposed algorithm achieves a detection speed of 14.38 fps with a false detection rate of 3.64%, satisfying the operational demands for autonomous driving of jujube harvesters. Furthermore, this algorithm can be extended to other orchard mobile robots that execute row-straddling operations similar to jujube harvesters.