ArticleSensors (Basel, Switzerland)2021
Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building.Biomimetics (Basel, Switzerland) · 2024Article
- Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script.Sensors (Basel, Switzerland) · 2023Article
- Spatio-Temporal Calibration of Multiple Kinect Cameras Using 3D Human Pose.Sensors (Basel, Switzerland) · 2022Article
- Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons.Sensors (Basel, Switzerland) · 2022Article
- 3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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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.
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