Abstract
We describe a new approach to absolute pose estimation from noisy and outlier contaminated matching point sets for RGB-D sensors. We show that by integrating multiple forms of correspondence based on 2-D and 3-D points and surface normals gives more precise, accurate and robust pose estimates. This is because it gives more constraints than using one form alone and increases the available measurements, especially when dealing with sparse matching sets. We demonstrate the approach by incorporating it within a RANSAC algorithm and introduce a novel direct least-square approach to calculate pose estimates. Results from experiments on synthetic and real data demonstrate improved performance over existing methods.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Robotics and Automation (ICRA 2016) |
Subtitle of host publication | Proceedings of a meeting held 16-21 May 2016, Stockholm, Sweden |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 4756-4761 |
Number of pages | 6 |
ISBN (Electronic) | 9781467380263 |
ISBN (Print) | 9781467380270 |
DOIs | |
Publication status | Published - Aug 2016 |
Event | 2016 IEEE International Conference on Robotics and Automation (ICRA) - Stockholm, Sweden Duration: 16 May 2016 → 21 May 2016 |
Conference
Conference | 2016 IEEE International Conference on Robotics and Automation (ICRA) |
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Country/Territory | Sweden |
City | Stockholm |
Period | 16/05/16 → 21/05/16 |
Keywords
- computer vision
- robotics
- pose estimation
- SLAM
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Professor Andrew Calway
- School of Computer Science - Professor of Computer Vision
- Visual Information Laboratory
Person: Academic , Member