Robot localization and map building based on hybrid features

Aiming at the problem that the original ORBSLAM algorithm only uses point features to estimate pose, which leads to the degradation of the algorithm accuracy and robustness in the scene with insufficient point features. In order to improve the efficiency and accuracy of the algorithm, the original algorithm is improved. Firstly, this paper builds a binary environment dictionary, which can increase the dictionary loading speed of the algorithm. Secondly, an improved RANSAC algorithm is proposed to increase the elimination efficiency in eliminating feature mismatching. Then, a hybrid feature model is established to extract the point features and line features in the image to estimate pose. Finally, according to the pose graph optimization model, a global Bundle Adjustment algorithm based on the maximum common view weight frames is proposed and used in pose optimization, and the establishing of the dense and sparse maps is realized. The experiment results show that the hybrid feature model can effectively reduce the algorithm error. With the binary environment dictionary, the loading time of the dictionary is decreased from 14.945 seconds to 0.420 seconds. The execution time of the improved RANSAC algorithm is reduced from 0.803 seconds to 0.078 seconds. Using TUM dataset to verify the algorithm, the root mean square error of the camera trajectory is decreased by 2.27 cm and 4.87 cm, respectively. The experiment results on the dataset prove the effectiveness of the proposed algorithm. © 2018, Science Press. All right reserved.