International audienceIn order to obtain accurate position estimation, it is imperative that the SPIDAR, a haptic interface device, is properly calibrated. The driving idea of this work is to derive easy-to-use calibration algorithms that can be used to calibrate our haptic device and to add therefore adaptability to the system behavior. We make use of regression methods and we obtain calibration algorithms as a solution to SPIDAR inaccuracy. The efficacy of the proposed methods is illustrated using experimental data collected from a sensor platform

Abstract-The aim of this paper is to asses to what extent an optical tracking system (OTS) used for position tracking in virtual reality can be improved by combining it with a human scale haptic device named Scalable-SPIDAR. The main advantage of the Scalable-SPIDAR haptic device is the fact it is unobtrusive and not dependent of free line-of-sight. Unfortunately, the accuracy of the Scalable-SPIDAR is affected by bad-tailored mechanical design. We explore to what extent the influence of these inaccuracies can be compensated by collecting precise information on the nonlinear error by using the OTS and applying support vector regression (SVR) for calibrating the haptic device reports. After calibration of the Scalable-SPIDAR we have found that the average error in position readings reduced from to 263.7240±75.6207 mm to 12.6045±8.4169 mm. These results encourage the development of a hybrid hapticoptical system for virtual reality applications where the haptic device acts as an auxiliary source of position information for the optical tracker.

In this research, a simple, yet, efficient calibration procedure is presented in order to improve the accuracy of the Scalable-SPIDAR haptic device. The two-stage procedure aims to reduce discrepancies between measured and actual values. First, we propose a new semiautomatic procedure for the initialization of the haptic device. To perform this initialization with a high level of accuracy, an infrared optical tracking device was used. Furthermore, audio and haptic cues were used to guide the user during the initialization process. Second, we developed two calibration methods based on regression techniques that effectively compensate for the errors in tracked position. Both Neural Network (NN) and Support Vector Regression (SVR) methods were applied to calibrate the position errors present in the haptic device readings. A comparison between these two regression methods was carried out to show the underlying algorithm and to indicate the inherent advantages and limitations for each method. Initial evaluation of the proposed procedure indicated that it is possible to improve accuracy by reducing the Scalable-SPIDAR's average absolute position error to about 6 mm within a 1m x 1m x 1m workspace.

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