Research On Improving The Robustness Of Visual SLAM

Visual SLAM(Visual Simultaneous Localization and Mapping)refers to the acquisition of environmental information through visual sensors and the processing of this information through algorithms,while simultaneously estimating the device’s own position and constructing an environmental map.Visual SLAM technology has a wide range of applications in fields such as autonomous driving,robot navigation,and virtual reality.However,in practical applications,there are still many challenges to improving the robustness and reliability of visual SLAM systems,such as the complexity and variability of the environment and the non-uniform motion of the camera.Therefore,research on improving the robustness of visual SLAM systems is of great significance.The core of visual SLAM system is data association and optimization.This thesis focused on the issues of robustness of visual SLAM systems in different working scenarios and conducted research on improving the robustness of visual SLAM systems from three aspects:(1)research on improving the robustness of visual SLAM in high-texture static scenes;(2)research on improving the robustness of visual SLAM in low-texture static scenes;(3)research on improving the robustness of visual SLAM in dynamic scenes.Based on the above research directions,this thesis systematically analyzed and improved the shortcomings of current research on improving the robustness of visual SLAM.Accordingly,this thesis carried out the following research work:1.This thesis focused on improving the robustness of visual SLAM in high-texture static scenes by addressing the problems of camera motion modeling and feature point extraction and matching in large dynamic range scenes.In order to address the issue that the constant velocity motion model could not well characterize the actual camera motion,this thesis first analyzed the errors in data association using the constant velocity motion model from a theoretical perspective,and then proposed a constant acceleration motion model based on the analysis results.The experimental results on the TUM dataset showed that the proposed constant acceleration motion model could effectively improve the system’s accuracy.Specifically,on the test sequence with loop closures,the trajectory estimation accuracy of the visual SLAM system using the constant acceleration motion model was improved by 5.0% for the root mean square error(RMSE)of absolute trajectory error(ATE),compared to that using the constant velocity motion model.On the test sequence without loop closures,the accuracy was improved by 26.7% and 23.9%.To address the problem of feature point extraction and matching in large dynamic range scenes,this thesis adopted the Retinex image decomposition theory and the image illumination equalization algorithm to equalize the brightness information of the image.The experimental results showed that the image after processing by the illumination equalization algorithm was more likely to complete feature point extraction and matching in some dark areas.2.This thesis addressed the problem of the drastic decline in the robustness of visual SLAM based on image feature points in low-texture scenes by introducing line features and plane features.To address the issues of slow matching speed and complex matching steps in traditional plane matching algorithms,this thesis proposed a fast plane matching algorithm.The time complexity of each step in the proposed algorithm was O(1),and the overall time complexity was O(n).Experimental results showed that the proposed fast plane matching algorithm had significantly lower average and maximum time consumption for each step than traditional algorithms,with much smaller variance.Moreover,the proposed algorithm achieved higher accuracy in map building.Based on point,line,and plane features,this thesis proposed a multi-feature fusion positioning and mapping solution,which effectively reduced the system’s dependence on image point features.Experimental results on the TUM dataset with structureless low-texture test sequences demonstrated the effectiveness of the proposed algorithm.Considering that visual SLAM systems are prone to drift due to cumulative errors,this thesis adopted a low-drift camera pose estimation solution based on a Manhattan coordinate system.Furthermore,based on the algebraic relationship of camera pose transformation in 3D space,this thesis further proposed a low-drift camera pose estimation solution based on a non-orthogonal coordinate system in 3D space.Experimental results showed that the proposed low-drift camera pose estimation solution achieved an accuracy improvement of 7.1% and 12.5% in terms of the average and root mean square error of absolute trajectory error,respectively,compared to the original system.3.To address the problem of visual SLAM systems being easily disturbed by moving objects in dynamic scenes,this thesis adopted a semantic segmentation neural network to extract regions of actively moving objects in the scene,and then further extracted the outer contours of these regions.Based on the distance between feature points and the outer contour,this thesis divided the extracted feature points into different sets based on their motion characteristics.Then,based on the ICP algorithm,the camera pose was initialized on a priori stationary feature point set.This rough initialization pose was used together with the scene flow geometric constraint and the epipolar geometric constraint to detect the motion consistency of the priori passive motion feature point set,and finally obtained the stationary feature point set,which was used for accurate camera pose tracking.Experimental results showed that the proposed solution achieved an accuracy improvement of 84.5%-98.4% in terms of the root mean square error of absolute pose error,and24.0%-65.2% in terms of the root mean square error of relative pose error on dynamic scene test sequences of the TUM dataset compared to the original system.