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

被引:139
作者
Kim, Deok-Hwa [1 ]
Kim, Jong-Hwan [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 34141, South Korea
基金
新加坡国家研究基金会;
关键词
Background subtraction; dynamic environment; simultaneous localization and mapping (SLAM); visual odometry; visual tracking;
D O I
10.1109/TRO.2016.2609395
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
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.
引用
收藏
页码:1565 / 1573
页数:9
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