Pixel-Wise Motion Segmentation for SLAM in Dynamic Environments
Pixel-Wise Motion Segmentation for SLAM in Dynamic Environments
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DOI：
10.1109/access.2020.3022506
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Al-Hamadi, Ayoub
中科院分区：
文献类型：
--
作者：
Hempel, Thorsten;Al-Hamadi, Ayoub
视觉同步定位和建图 (SLAM) 是许多移动机器人系统的关键先决条件。 SLAM 方法的常见假设是静态环境。动态物体的干扰可能会导致相机姿态跟踪受损和地图永久变形。这限制了许多视觉 SLAM 系统在动态环境典型的现实场景中的使用。我们提出了一种基于场景流模型估计的动态图像序列逐像素分割的新方法。我们通过单独评估每个像素运动来稀疏地检测和消除外围像素，并为 SLAM 维护静态场景背景的最大可能区域。对公共 TUM 数据集的评估表明，我们提出的方法优于其他同类最先进的 SLAM 系统动态移除方法。
Visual simultaneous localization and mapping (SLAM) is a key prerequisite for many mobile robotic systems. A common assumption for SLAM methods is a static environment. The interference of dynamic objects can lead to impairment of the camera pose tracking and permanent distortions of the map. This limits the use of many visual SLAM systems in real world scenarios, where dynamic environments are typical. We present a novel method for pixel-wise segmentation of dynamic image sequences based on a scene flow model estimation. We detect and eliminate outlying pixels sparsely by evaluating each pixel motion separately and maintain the most possible area of static scene background for SLAM. The evaluation with the public TUM dataset demonstrates that our proposed method outperforms other comparable state-of-the-art approaches for dynamic removal for SLAM systems.