The invention discloses an automatic loading method for a
truck based on
deep learning and stereoscopic vision. The automatic loading method comprises the following steps that S1, goods location pixel coordinates are obtained and converted into an AGV forklift coordinate
system; s2, a driving track is calculated, a front
station is generated, and the AGV forklift is guided to move; s3, collecting
point cloud data, denoising, extracting features by adopting an improved Transform
point cloud registration network, calculating three-dimensional feature points of goods locations and trays, and converting coordinates; s4, calculating a rotation adjustment amount and a displacement compensation parameter, optimizing a driving track and correcting a
butt joint deviation; s5, adopting a self-adaptive local attention weighting strategy to match the target goods allocation with the tray slot, and correcting the
pose; s6, the cargo stacking posture is adjusted,
gravity center distribution is optimized, and the AGV forklift is controlled to complete cargo loading; and S7, exiting the loading area along the optimized track, recording data and optimizing a loading strategy. The goods allocation identification precision is improved, the loading path is optimized, manual intervention is reduced, the loading efficiency, stability and adaptability are improved, and the
logistics automation level is enhanced.