Improving RGB-D SLAM in dynamic environments: A motion removal approach
Improving RGB-D SLAM in dynamic environments: A motion removal approach
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DOI：
10.1016/j.robot.2016.11.012
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发表时间：
2017-03-01
影响因子：
4.3
通讯作者：
Meng, Max Q. -H.
中科院分区：
文献类型：
--
作者：
Sun, Yuxiang;Liu, Ming;Meng, Max Q. -H.
基于RGB-D数据的视觉同时定位与地图构建（Visual Simultaneous Localization and Mapping，SLAM）是近几十年来机器人感知的基本方法。存在关于RGB-D SLAM及其应用的大量文献。然而，大多数现有的RGB-D SLAM方法假设在SLAM过程期间遍历的环境是静态的。这是因为在动态环境中移动对象会严重降低SLAM性能。静态世界假设限制了RGB-D SLAM在动态环境中的应用。为了解决这个问题，我们提出了一种新的基于RGB-D数据的运动去除方法，并将其集成到RGB-D SLAM的前端。运动去除方法充当预处理阶段以过滤掉与移动对象相关联的数据。我们使用公共RGB-D数据集进行了实验。实验结果表明，该运动消除方法能够有效地提高RGB-D SLAM在各种复杂动态环境中的性能。(C)© 2016 Elsevier B. V.版权所有。
Visual Simultaneous Localization and Mapping (SLAM) based on RGB-D data has developed as a fundamental approach for robot perception over the past decades. There is an extensive literature regarding RGB-D SLAM and its applications. However, most of existing RGB-D SLAM methods assume that the traversed environments are static during the SLAM process. This is because moving objects in dynamic environments can severely degrade the SLAM performance. The static world assumption limits the applications of RGB-D SLAM in dynamic environments. In order to address this problem, we proposed a novel RGB-D data-based motion removal approach and integrated it into the front end of RGB-D SLAM. The motion removal approach acted as a pre-processing stage to filter out data that were associated with moving objects. We conducted experiments using a public RGB-D dataset. The results demonstrated that the proposed motion removal approach was able to effectively improve RGB-D SLAM in various challenging dynamic environments. (C) 2016 Elsevier B.V. All rights reserved.