Robust Calibration of a Multi-View Azure Kinect Scanner Based on Spatial Consistency

Darwish, Walid; Bolsée, Quentin; Munteanu, Adrian

doi:10.1109/ic3d51119.2020.9376321

Cited by 1 publication

(1 citation statement)

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“…In the literature, several calibration methodologies for cameras with overlapping Field-of-Views (FOVs) cameras have been studied [23][24][25][26]. Most of them are based on the use of targets whose features can be robustly found.…”

Section: Introductionmentioning

confidence: 99%

Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Romeo

Marani

Perri

et al. 2022

Sensors

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Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm.

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Section: Introductionmentioning

confidence: 99%