DESIGN OF AN UNMANNED TRANSFER VEHICLE LOOP DETECTION SYSTEM FOR GRAIN DEPOT SCENARIOS

The grain depot scenario is critical for grain logistics and transportation, and it is also a key setting for the efficient operation of intelligent grain logistics platform vehicles. A large number of repetitive and specific building structures, along with low-textured walls, characterize the grain depot scene. Loopback detection is an essential module in visual SLAM, and an efficient system can eliminate accumulated errors. While traditional systems rely on manually designed features, which struggle to adapt to the unique grain depot environment, this paper proposes a deep learning-based loopback detection system for grain transfer trucks. Leveraging a custom dataset capturing both grain depot environments and loopback scenarios, the system employs convolutional neural networks for identifying building equipment and door numbers, edge extraction for robust feature matching, and image template matching for efficient loopback verification. Extensive testing on the grain depot loopback dataset demonstrates that the system significantly improves loopback detection accuracy and efficiency, paving the way for reliable autonomous navigation in grain depots.