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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dryanovski, I. Valenti, R.G. Jizhong Xiao |
| Copyright Year | 2013 |
| Description | Author affiliation: Electr. Eng. Dept., City Coll. of New York, New York, NY, USA (Valenti, R.G.; Jizhong Xiao) || Dept. of Comput. Sci., City Univ. of New York (CUNY), New York, NY, USA (Dryanovski, I.) |
| Abstract | An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques. |
| Starting Page | 2305 |
| Ending Page | 2310 |
| File Size | 1484277 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467356411 |
| ISSN | 10504729 |
| e-ISBN | 9781467356435 |
| DOI | 10.1109/ICRA.2013.6630889 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-06 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Uncertainty Cameras Trajectory Data models Iterative closest point algorithm Visualization Robot vision systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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