Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion; This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algorithms, and proposes an improved algorithm in view of multi-sensor fusion to improve tracking accuracy. The experiment shows that the average Root-mean-square deviation of the position of the improved mobile robot vision algorithm in view of multi feature fusion is 0.02 in nine data packets of the Tum dataset; The average Root-mean-square deviation of the position of the data packet successfully tracked by the traditional wheeled robot vision algorithm is 0.016; It improved the average accuracy by 11.11%, which is 31.03% higher than the average accuracy of the Manhattan wheeled robot vision algorithm. Compared to the multi feature fusion based vision improvement algorithm for mobile robots and the closed-loop detection based multi-sensor improvement algorithm, the accuracy of the closed-loop detection based multi-sensor improvement algorithm has increased by 0.655% and 10.47%, respectively. The outcomes indicate that the improved algorithm can improve the accuracy of mobile robot tracking, thereby expanding its application range.
INDEX TERMSMulti feature fusion, Mobile robots, Visual algorithms, Multi sensor fusion, Encoder