Abstract
RGB-D cameras have been commercialized, and many applications using them have been proposed. In this paper, we propose a robust registration method of multiple RGB-D cameras. We use a human body tracking system provided by Azure Kinect SDK to estimate a coarse global registration between cameras. As this coarse global registration has some error, we refine it using feature matching. However, the matched feature pairs include mismatches, hindering good performance. Therefore, we propose a registration refinement procedure that removes these mismatches and uses the global registration. In an experiment, the ratio of inliers among the matched features is greater than 95% for all tested feature matchers. Thus, we experimentally confirm that mismatches can be eliminated via the proposed method even in difficult situations and that a more precise global registration of RGB-D cameras can be obtained.
Highlights
RGB-D cameras such as Azure Kinect DK [1,2,3] and RealSense D400 series [4,5], have been commercialized
We proposed a method to fine the global registration between multiple RGB-D cameras
In the first module of the proposed method, a human body tracking system based on deep learning provided by Azure Kinect SDK is used in order to find the global registration between two cameras
Summary
RGB-D cameras such as Azure Kinect DK [1,2,3] and RealSense D400 series [4,5], have been commercialized. The global registration is the type of pose estimation using cameras. In order to apply the global registration, feature points are generally matched between cameras. In order to remove these mismatches, post-processing, such as random sample consensus (RANSAC) [6,7], is required. This type of post-processing shows lower performance when the number of mismatches is high. We propose a global registration method using body tracking and refinement registration with geometrical information.
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