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research:vslam:dso [2018/01/02 13:41] Rui Wang |
research:vslam:dso [2018/01/06 23:24] Rui Wang |
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| - | < | + | ====== DSO: Direct Sparse Odometry |
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| The following 4 figures show the average translational and rotational errors with respect to driving intervals (first row) and driving speed (second row) on the KITTI VO testing set. We compare our method with the current state-of-the-art direct and feature-based methods, namely the Stereo LSD-SLAM and ORB-SLAM2. Note that both of the compared methods are SLAM systems with loop closure based on pose graph optimization (ORB-SLAM2 also with global bundle adjustment), | The following 4 figures show the average translational and rotational errors with respect to driving intervals (first row) and driving speed (second row) on the KITTI VO testing set. We compare our method with the current state-of-the-art direct and feature-based methods, namely the Stereo LSD-SLAM and ORB-SLAM2. Note that both of the compared methods are SLAM systems with loop closure based on pose graph optimization (ORB-SLAM2 also with global bundle adjustment), | ||
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| As qualitative results we run our method on all the sequences from the training set and compare the estimated camera trajectories to the provided ground truth. Following are the results on some example sequences. | As qualitative results we run our method on all the sequences from the training set and compare the estimated camera trajectories to the provided ground truth. Following are the results on some example sequences. | ||
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| - | **Update July 2017: ** After the ICCV 2017 deadline, we extended our method to a SLAM system with additional components for map maintenance, | + | **Update July 2017: ** After the ICCV 2017 deadline, we extended our method to a SLAM system with additional components for map maintenance, |
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