LAUSR

Aiming at the real intestinal environment, the commonly used feature extraction algorithms are compared and analyzed. The performances of SIFT, BRISK, ORB and FREAK are evaluated from three aspects: feature extraction time, correct matching rate and attitude estimation accuracy under rotation and scaling transformation. ORB binary feature operator has the advantages of fast feature extraction, high correct matching rate, and small estimation error. Based on the measured data of gastrointestinal endoscopy, the classical SLAM algorithm is modified in the aspects of pose adjustment and spatial point location calculation. A framework of gastrointestinal SLAM (Simultaneous Localization and Mapping) algorithm is constructed by introducing the local pose optimization algorithm based on visual correlation graph and triangular measurement algorithm with the minimum geometric distance. In order to tackle the problem of a large amount of accumulated data of spatial points and keyframes, a keyframe judgment strategy and spatial map point deletion strategy is designed to control the growth of keyframes and map points. The feature map of the human colon is reconstructed based on endoscopic video data, and the trajectory of the endoscope is estimated, which verifies the feasibility of the algorithm in the actual intestinal environment. In order to get the real value of map and trajectory conveniently, a simulated gastrointestinal system is built to validate the effectiveness of the algorithm, and the effects of the image frame rate and a number of keyframes on positioning accuracy and feature map accuracy are evaluated. Based on the simulated gastrointestinal system, the SLAM algorithm of the gastrointestinal tract is compared with the traditional visual mileage calculation method. When the endoscope moves in a straight line, keyframe storage of the proposed algorithm is only 1/8 of that of the latter, and the positioning accuracy is improved three times.