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Vision-Inertial-Encoder RGB-D SLAM System Proposed

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Zhanghao Zhu and other authors propose a tightly coupled vision-inertial-encoder RGB-D SLAM system for differential-drive wheeled robots. The authors state that they establish a generative measurement model for the encoder and derive the maximum a posteriori estimation expression and the Jacobians required for optimization. On a self-built dataset, the system's real-time state estimation has a root-mean-square error of approximately 2–7 cm; the authors state that the system is intended to compensate for the failure of pure vision solutions when feature points are temporarily missing and during fast motion. The source code and the dataset containing encoder information have been made public.

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  1. Visual-Inertial-Encoder RGB-D SLAM for Differential Wheeled Robots

    The study proposes a generative measurement model for encoders and derives the maximum a posteriori estimation expression and the Jacobians required for optimization, building a tightly coupled visual-inertial-encoder RGB-D SLAM system. On a self-built dataset, the system's real-time state estimation achieves a root mean square error of about 2-7 cm, used to compensate for the failure of pure vision solutions during temporary feature point loss and fast motion. The source code and the dataset containing encoder information have been made public.

    arXiv cs.RO ·

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Perception and localizationData and simulationResearch papers

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