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首页> 外文会议>IEEE International Conference on Information and Automation;IEEE International Conference on Automation and Logistics >Removing dynamic 3D objects from point clouds of a moving RGB-D camera
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Removing dynamic 3D objects from point clouds of a moving RGB-D camera

机译：从移动的RGB-D摄像机的点云中删除动态3D对象

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摘要

Most state-of-the-art visual simultaneous localization and mapping (SLAM) systems are designed for applications in static environments. However, during a SLAM process, dynamic objects in the field-of-view of the camera will affect the accuracy of visual odometry and loop-closure detection. In this paper, we present a solution to removing dynamic objects from RGB images and their corresponding depth images when a RGB-D camera is mounted on a mobile robot for visual SLAM. We transform two selected successive images to the same image coordinate frame through feature matching. Then we detect candidate image pixels of dynamic objects by applying a threshold to the image difference between the two images. Furthermore, we utilize depth information of the candidate pixels to decide whether true dynamic objects are found. Finally, in order to extract a complete 3-dimensional (3D) dynamic object, we find the correspondence between the object and a cluster of the point cloud computed from RGB-D images. To evaluate the performance of detecting and removing dynamic objects, we do experiments in various indoor scenarios, which demonstrate the efficiency of the proposed algorithm.
机译：大多数最先进的视觉同时定位和制图（SLAM）系统都是为静态环境中的应用程序设计的。但是，在SLAM过程中，摄像机视场中的动态对象将影响视觉测距法和闭环检测的准确性。在本文中，我们提出了一种解决方案，当将RGB-D摄像机安装在用于视觉SLAM的移动机器人上时，可以从RGB图像及其对应的深度图像中删除动态对象。我们通过特征匹配将两个选定的连续图像转换为相同的图像坐标系。然后，我们通过对两个图像之间的图像差异应用阈值来检测动态对象的候选图像像素。此外，我们利用候选像素的深度信息来确定是否找到了真正的动态对象。最后，为了提取完整的3D（3D）动态对象，我们找到了对象与根据RGB-D图像计算出的点云簇之间的对应关系。为了评估检测和去除动态物体的性能，我们在各种室内场景中进行了实验，证明了该算法的有效性。

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