A multi-feature fusion RGB-D SLAM algorithm considering constraint degradation is proposed to solve the problem of tracking loss and reduced reconstruction accuracy in SLAM sys-tem due to insufficient number of feature points in indoor scenes with weak texture.In order to exploit the constraints provided by line and plane features for pose estimation,error equations for lines and planes are established respectively.By performing an eigenvalue decomposition of the Hessian matrix,the degradation of pose constraints imposed by line and plane features is quantita-tively analyzed,paving the way for the establishment of a multi-feature fusion objective optimiza-tion function that considers constraint degradation.In addition,by exploiting the Manhattan World assumption,a Manhattan coordinate system is established to estimate the zero drift of the rotation matrix,providing accurate initial values to support plane matching and pose optimization.Experimental results show that after introducing line and plane features to establish the bundle ad-justment equation,the proposed method improves the trajectory accuracy on the low-texture dataset ICL-NUIM by 37.5％compared to the benchmark ORB-SLAM2,effectively improving the trajectory accuracy of SLAM systems in weakly textured environments.
Simultaneous localization and mapping(SLAM)Multi-feature fusionIndoor weak texture scenesManhattan World assumptionRGB-D cameraConstrained degradation
维普
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