Junfu Qiao, Jinqin Guo, and Yongwei Li
Robot, accuracy, indoor
Indoor positioning and navigation of robots have become increasingly important for various applications, including manufacturing, logistics, and home automation. In this study, we propose a vision- based method for robot indoor positioning and navigation. Our approach utilises computer vision techniques to extract features from the environment and estimate the robot’s position and orientation. Key facets explores encompasses visual sensing, feature extraction, mapping, localisation, path planning, and control strategies. An emphasis is placed on how theme components synergise to form a holistic vision-based approach tailored for indoor robot navigation. The integration of theme methods with existing robotic platforms is underscored, facilitating their seamless incorporation into a wide array of applications. This review paper consolidates the current state of knowledge, portraying vision-based indoor navigation as a transformative technology poised to revolutionise robotic operations within indoor environments.
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