Mathematical elements for computer graphics (2nd ed.)
Mathematical elements for computer graphics (2nd ed.)
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inertial and magnetic posture tracking for inserting humans into networked virtual environments
VRST '01 Proceedings of the ACM symposium on Virtual reality software and technology
Optimal pitch map generation for scanning pitch design in selective sampling
Robotics and Autonomous Systems
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Working in the low cost 3D scanner design domain, it would be very interesting to employ the inertial technologies because they could provide objects' surface spatial data, recording their movements, and asking a very low cost in term of sensor investment. Unfortunately these technologies are characterized by distortion problems that normally do not allow to obtain satisfying measures for being employed for 3D scanning applications. This situation happens when working with Magnetic Angular Rate Gravity (MARG) sensor, on which many reports have been written to describe the methods used to suitably manage the data provided by the sensors in order to obtain an accurate orientation estimation; but only a few address the problem of calibration and distortion compensation. Furthermore, the proposed approaches usually involve both complex sensors models and accurate calibration facilities expensive from the workload, the computational and the economic points of view which compromise their possible employment in low-cost 3D scanning applications. In this paper, a novel approach for MARG sensors heading alignment and distortion compensation is proposed in order to increase the reliability of the information provided by the sensors and improve the process of attitude estimation, in order to get measurement quality level sufficient to be employable in 3D scanning applications. Both the effectivity and the reliability of the proposed approach are validated by some experimental results and the performances are evaluated considering the quality of the outcome provided by the same attitude estimation algorithm processing raw data and compensated data.