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A benchmark for rgb-d visual odometry, 3d reconstruction and slam.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ankur, H. Whelan, Thomas Mcdonald, John Davison, Andrew J. |
| Abstract | for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Rgb-d Visual Odometry Visual Odometry Surface Reconstruction Accuracy Generated Environment Final Map Rgb-d Community Ground Truth Surface Model Perfect Ground Truth Pose Main Focus Rgb-d Sequence Rgb-d Data Synthetic Imagery Handheld Rgb-d Camera Sequence Depth Data Observed Real-world Artefact Slam Trajectory Estimation Rgb-d Datasets Sensor Noise |
| Content Type | Text |
