The Research On The Technology Of Visual SLAM System Performance Optimization

Simultaneous Localization and Mapping(SLAM)is a key technology for autonomous navigation and positioning of robots.In the past ten years,the visual SLAM system with camera as sensor has developed a variety of algorithms including indirect method,direct method and hybrid method.These algorithms can complete basic visual positioning and the task of building a map model.For practical applications such as virtual reality/augmented reality,current algorithms cannot handle camera pose estimation for some complex motion well.In addition,some front-end algorithms do not take loopclosure detection into account.Therefore,there is still much room for improvement in terms of the performance of the visual SLAM algorithm.The existing visual odometry method can handle normal six-degree-of-freedom camera motion well,but it has a poor processing effect on degenerate motion types,especially in many virtual reality/augmented reality applications.To this end,this paper proposes a Paused Supplementary Information Mechanism(PSIM)to monitor real-time changes in camera pose information and respond to conditions.The mechanism first calculates and judges two different motion trends of the camera through motion vectors and the running state of the algorithm and processes the two types of motion data separately.Then,in the case of ordinary motion,the algorithm selects the direct method to solve the camera pose information.In this case,the method of 3D-2D and homography matrix are selected to solve the camera pose of the rotating motion,and finally the camera pose information of the complete trajectory is obtained through the back-end data optimization.The experimental results of the public dataset show that the mechanism can effectively improve the robustness of the algorithm to deal with multiple motion states.The loop-closure detection module can well extend the visual odometry calculation method to the system and expand the applicable range of the front-end algorithm.At present,research in this area is insufficient.To this end,this paper proposes a new loopclosure detection method that uses a spatial pyramid model to match images,establishes a dictionary package of BRISK features for the dataset,and applies it in the direct method visual odometry method to test the loop detection to the direct method SLAM precision affects.Through testing on public datasets,the experimental results show that the loop-closure detection method in this paper can effectively improve the accuracy of the direct method SLAM in trajectory and pose calculation,and can effectively reduce the redundancy of the map to the same location.