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| Content Provider | ACM Digital Library |
|---|---|
| Author | Dong, Haiwei Saddik, Abdulmotaleb El Figueroa, Nadia |
| Copyright Year | 2015 |
| Abstract | We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps. |
| Starting Page | 1 |
| Ending Page | 10 |
| Page Count | 10 |
| File Format | |
| ISSN | 21576904 |
| e-ISSN | 21576912 |
| DOI | 10.1145/2629673 |
| Volume Number | 6 |
| Issue Number | 2 |
| Journal | ACM Transactions on Intelligent Systems and Technology (TIST) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-03-31 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Indoor mapping Benchmark datasets Evaluation Kinect |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Theoretical Computer Science |
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