Effective Background Model-Based RGB-D Dense Visual Odometry in a Dynamic Environment

This paper proposes a robust background model-based dense-visual-odometry (BaMVO) algorithm that uses an RGB-D sensor in a dynamic environment. The proposed algorithm estimates the background model represented by the nonparametric model from depth scenes and then estimates the ego-motion of the sens...

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Bibliographic Details
Published inIEEE transactions on robotics Vol. 32; no. 6; pp. 1565 - 1573
Main Authors Kim, Deok-Hwa, Kim, Jong-Hwan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Background subtraction
Computational modeling
dynamic environment
Effectiveness
Experiments
Heuristic algorithms
Robot sensing systems
Robotics
Robustness
Sensors
simultaneous localization and mapping (SLAM)
Vehicle dynamics
visual odometry
visual tracking
Visualization
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Summary:This paper proposes a robust background model-based dense-visual-odometry (BaMVO) algorithm that uses an RGB-D sensor in a dynamic environment. The proposed algorithm estimates the background model represented by the nonparametric model from depth scenes and then estimates the ego-motion of the sensor using the energy-based dense-visual-odometry approach based on the estimated background model in order to consider moving objects. Experimental results demonstrate that the ego-motion is robustly obtained by BaMVO in a dynamic environment.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2016.2609395