RGB-D SLAM in Dynamic Environments Using Static Point Weighting
RGB-D SLAM in Dynamic Environments Using Static Point Weighting
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DOI：
10.1109/lra.2017.2724759
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发表时间：
2017-10-01
影响因子：
5.2
通讯作者：
Lee, Dongheui
中科院分区：
文献类型：
--
作者：
Li, Shile;Lee, Dongheui
针对动态环境，提出了一种基于实时深度边缘的RGB-D SLAM系统。我们的视觉里程计方法是基于帧到关键帧的配准，其中只使用深度边缘点。为了减少动态目标的影响，我们提出了一种关键帧边缘点的静态加权方法。静态权重表示一个点成为静态环境一部分的可能性。将该静态权值加入到强度辅助迭代最近点（iicp）方法中执行配准任务。此外，我们的方法被集成到SLAM（同时定位和映射）系统中，其中使用了有效的闭环检测策略。我们的视觉里程计方法和SLAM系统都使用来自TUM RGB-D数据集的具有挑战性的动态序列进行了评估。与当前动态环境下的跟踪方法相比，该方法显著降低了跟踪误差。
We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the keyframe. Static weight indicates the likelihood of one point being part of the static environment. This static weight is added into the intensity assisted iterative closest point (IAICP) method to perform the registration task. Furthermore, our method is integrated into a SLAM (Simultaneous Localization and Mapping) system, where an efficient loop closure detection strategy is used. Both our visual odometry method and SLAM system are evaluated with challenging dynamic sequences from the TUM RGB-D dataset. Compared to state-of-the-art methods for dynamic environment, our method reduces the tracking error significantly.