Figure 7 - uploaded by Hao Fu
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The matching error in different search window size. The abscissa represents the size of the search window, the unit is the pixel; the ordinate represents the error of matched points, the unit is the pixel 2 .
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... In this article, a new dynamic keypoint removing method is proposed to increase the accuracy and robustness in the dynamic indoor environment on localization and mapping. It combines semantic segmentation network Deeplabv3 (Chen et al., 2017) with moving detection to exclude dynamic region of images. The main contributions of this paper are as follows: 30 1. Sparse optical flow and epipolar geometry are combined to conduct moving detection; and moving detection is combined with the segmentation result to perform dynamic object removal. ...
In this study, a robust and accurate SLAM method for dynamic environments is proposed. Sparse optical flow and epipolar geometric constraint are combined to conduct moving detection by judging whether a priori dynamic object is in motion. Semantic segmentation is combined with moving detection to perform dynamic keypoints removal by removing dynamic objects. The dynamic objects removal method is integrated into ORB-SLAM2, enabling robust, accurate localization and mapping. Experiments on TUM datasets show that compared with ORB-SLAM2, the proposed system can significantly reduce the pose estimation error, and the RMSE and S.D. of ORB-SLAM2 are reduced by up to 97.78% and 97.91% respectively under high dynamic sequences, improving the robustness in dynamic environments. Compared with other similar SLAM methods, the RMSE and S.D. of the proposed method are reduced by up to 69.26% and 73.03% respectively. Dense semantic maps built with our method are also much closer to the groundtruth.
