Addressing the challenge of visual simultaneous localization and mapping(SLAM)systems predominantly relying on static environment assumptions and exhibiting inaccurate pose estimation in dynamic scenes,a visual SLAM method combining sparse scene flow and weighted features in dynamic environment is proposed.Firstly,the SparseInst instance segmentation network is introduced to obtain semantic information in the environment and identify potential movable objects.Secondly,the relative motion of feature points in the camera coordinate system is obtained by calculating the sparse scene flow of feature points and their Mahalanobis distance,and the detection and removal of dynamic feature points are achieved by using the Chi-square test method.Then,static feature points are assigned weights,and a weighted objective function of the bundle adjustment optimization is designed to address the issue of ambiguous motion states of some feature points,thereby enhancing the localization accuracy of visual SLAM in dynamic scenes.Comparative experiments on the public datasets show that compared with ORB-SLAM2,YOLO-SLAM and SG-SLAM,the absolute trajectory root mean square error of the proposed method is reduced by an average of about 94.69％,27.55％,5.27％and 93.43％,38.30％,26.88％on the TUM RGB-D and Bonn datasets,respectively.
visual SLAMdynamic environmentsparse scene flowweighted features
维普
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