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We present a novel visual SLAM method for the warehouse robot with a singledownward-facing camera using ground textures. Traditional methods resort tofeature matching or point registration for pose optimization, which easilysuffers from repetitive features and poor texture quality. In this paper, wepresent a robust kernel cross-correlator for robust image-level registration.Compared with the existing methods that often use iterative solutions, ourmethod, named non-iterative visual SLAM (NI-SLAM), has a closed-form solutionwith a complexity of $O(n\log n)$. This allows it to run very efficiently, yetstill provide better accuracy and robustness than the state-of-the-art methods.In the experiments, we demonstrate that it achieves 78% improvement over thestate-of-the-art systems for indoor and outdoor localization. We havesuccessfully tested it on warehouse robots equipped with a single downwardcamera, showcasing its product-ready superiority in a real operating area.

Non-iterative SLAM for Warehouse Robots Using Ground Textures