Fast visual odometry and mapping from RGB-D data

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 acceler...

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Bibliographic Details
Main Authors: Dryanovski, Ivan, Valenti, Roberto G., Jizhong Xiao
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Data models
Iterative closest point algorithm
Robot vision systems
Trajectory
Uncertainty
Visualization
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Summary: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.
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2013.6630889