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This paper describes a brain-inspired simultaneous localization and mapping(SLAM) system using oriented features from accelerated segment test and rotated binary robust independent elementary(ORB) features of RGB(red,green,blue) sensor for a mobile robot.The core SLAM system,dubbed Rat SLAM,can construct a cognitive map using information of raw odometry and visual scenes in the path traveled.Different from existing Rat SLAM system which only uses a simple vector to represent features of visual image,in this paper,we employ an efficient and very fast descriptor method,called ORB,to extract features from RGB images.Experiments show that these features are suitable to recognize the sequences of familiar visual scenes.Thus,while loop closure errors are detected,the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm.Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.
This paper describes a brain-inspired simultaneous localization and mapping (SLAM) system using oriented features from accelerated segment test and rotated binary robust independent elementary (ORB) features of RGB (red, green, blue) sensor for a mobile robot. Core SLAM system, dubbed Rat SLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing Rat SLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RGB images.Experiments show that these features are suitable to recognize the sequences of familiar visual scenes.Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.