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research:vslam:dso [2018/01/06 23:27] Rui Wang |
research:vslam:dso [2018/08/24 13:41] Nan Yang |
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| Note that as for LSD-SLAM, we use a dual-licensing model; Please contact [[members: | Note that as for LSD-SLAM, we use a dual-licensing model; Please contact [[members: | ||
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| + | ===== Extensions ===== | ||
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| - | ====== Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras ====== | + | < |
| - | **Contact: | + | |
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| - | ===== Abstract ===== | + | |
| - | ** Stereo DSO ** is a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/ | + | |
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| - | ===== Results ===== | + | |
| - | For this work we use the [[http:// | + | |
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| - | ** KITTI Visual Odometry Benchmark ** | + | |
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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), | + | |
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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. | + | |
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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, | + | |
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| - | ** Frankfurt Sequence of Cityscapes** | + | |
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| - | To verify that our method can work with industrial level cameras (high dynamic range, rolling shutter with high pixel read-out speed), we evaluate our method on the Frankfurt sequence from the Cityscapes dataset. We split the sequence to several smaller segments, each with a comparable scale to those sequences from KITTI. The estimated camera trajectories with their alignments to the GPS trajectory are shown below (blue: estimates, red: GPS). Note that the provide GPS coordinates are not accurate. | + | |
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| - | Some qualitative results on the 3D reconstruction are shown below. | + | |
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| - | Under discussion. | + | |
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| ==== Publications ==== | ==== Publications ==== | ||
