Abstract
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 semi-automatic 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 networks and support vector regression 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 1 m × 1 m × 1 m workspace.
References
Advanced Realtime Tracking GmbH (2003) ARTtrack1 & DTrack—Manual Version 1.18
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press Inc, New York
Bloch G et al (2008) Support vector regression from simulation data and few experimental samples. Inf Sci 178(20):3813–3827
Boudoin P et al (2010) SPIDAR calibration based on neural networks versus optical tracking. BT—artificial neural networks and intelligent information processing. In: Proceedings of the 6th international workshop on artificial neural networks and intelligent information processing, pp 87–98
Bouguila L, Ishii M, Sato M (2000) Effect of coupling haptics and stereopsis on depth perception in virtual environment. In: Proceedings of the 1st workshop on haptic human computer interaction, 31st August–1st Sept 2000. pp 54–62
Briggs W (1999) Magnetic calibration by tetrahedral interpolation. In: Proceedings of NIST-ASME industrial virtual reality symposium, Chicago, pp 27–32
Bryson S (1992) Measurement and calibration of static distortion of position data from 3D trackers. In: Proceedings of SPIE conference. Stereoscopic displays and applications III, pp 244–255
Buoguila L, Ishii M, Sato M (2000) Multi-modal haptic device for large-scale virtual environments. In: Proceedings of the eighth ACM international conference on multimedia. MULTIMEDIA ’00. ACM, New York, pp 277–283
Burdea GC (1996) Force and touch feedback for virtual reality. Wiley, New York
Ellis SR et al (1999) Sensor spatial distortion, visual latency, and update rate effects\non 3D tracking in virtual environments. In: Proceedings IEEE virtual reality (Cat no 99CB36316), pp 218–221
Faroque S et al (2015) Haptic virtual reality training environment for micro-robotic cell injection. In: Kajimoto H, Ando H, Kyung K-U (eds) Haptic interaction: perception, devices and applications. Springer, Tokyo, pp 245–249
Fletcher C et al (2013) The development of an integrated haptic VR machining environment for the automatic generation of process plans. Comput Ind 64(8):1045–1060
Fuchs P, Moreau G, Guitton P (2011) Virtual reality: concepts and technologies, 1st edn. CRC Press Inc, Boca Raton
Ghazisaedy M et al (1995) Ultrasonic calibration of a magnetic tracker in a virtual reality space. In: Proceedings virtual reality annual international symposium ’95, pp 179–188
Group S (2005) SPIDAR-G/AHS1.0A user’s manual Ver. 2.0., pp 1–44
Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993
Harders M et al (2009) Calibration, registration, and synchronization for high precision augmented reality haptics. IEEE Trans Vis Comput Graph 15(1):138–149
Hastie TJ, Tibshirani RJ, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York
Hayward V et al (2004) Haptic interfaces and devices. Sensor Rev 24(1):16–29
Hirata Y, Sato M (1992) 3-dimensional interface device for virtual work space. In: Proceedings of the 1992 lEEE/RSJ international conference on intelligent robots and systems, vol 2, pp 889–896
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366
Huang J-N et al (1992) A comparison of projection pursuit and neural network regression modeling. Adv Neural Inf Process Syst 4:1159–1166
ISO 5725-1 (1994) Accuracy (trueness and precision) of measurement methods and results—part 1: general principles and definitions. International Organization for Standardization, Geneva
Ikits M et al (2001) An improved calibration framework for electromagnetic tracking devices. Proc IEEE Virtual Real 2001:63–70
Ikits M, Hansen CD, Johnson CR (2003) A comprehensive calibration and registration procedure for the visual haptic workbench. In: Proceedings of the workshop on virtual environments 2003. EGVE’03. ACM, New York, pp 247–254
Ikits M et al (2000) The visual haptic workbench. In: Proceedings of PHANToM users group workshop. pp 46–49
Jayaram U, Repp R (2002) Integrated real-time calibration of electromagnetic tracking of user motions for engineering applications in virtual environments. J Mech Des 124:623
Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT Press, Cambridge
Kecman V (2005) Support vector machines—an introduction. In: Wang L (ed) Support vector machines: theory and applications. Springer, Berlin, pp 1–47
Kenwright DN, Lane DA (1996) Interactive time-dependent particle tracing using tetrahedral decomposition. IEEE Trans Vis Comput Graph 2(2):120–129
Kim S et al (2002) Tension based 7-DOF force feedback device: SPIDAR-G. In: Proceedings, IEEE virtual reality. pp 283–284
Kindratenko V (1999) Calibration of electromagnetic tracking devices. Virtual Real 4:139–150
Kindratenko V (2000) A survey of electromagnetic position tracker calibration techniques. Virtual Real 5(3):169–182
Kindratenko V, Bennett A (2000) Evaluation of rotation correction techniques for electromagnetic position tracking systems. In: Mulder J, van Liere R (eds) Virtual environments 2000 SE—3. Eurographics. Springer, Vienna, pp 13–22
Kindratenko VV, Sherman WR (2005) Neural network-based calibration of electromagnetic tracking systems. Virtual Real. 9(1):70–78
Knoerlein B, Harders M (2011) Comparison of tracker-based to tracker-less haptic device calibration. In: World haptics conference (WHC), 2011 IEEE, pp 119–124
Kunzler U, Runde C (2005) Kinesthetic haptics integration into large-scale virtual environments. In: Eurohaptics conference, 2005 and symposium on haptic interfaces for virtual environment and teleoperator systems, 2005. World haptics 2005. First Joint, pp 551–556
Kwiatkowska EJ, Fargion GS (2003) Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. IEEE Trans Geosci Remote Sens 41(12):2844–2860
Livingston MA, State A (1997) Magnetic tracker calibration for improved augmented reality registration. Presence Teleoperators Virtual Environ 6(5):532–546
Melin P, Castillo O (2005) Studies in fuzziness and soft computing, volume 172. Soft Comput 18(3–4):318
Meyer K, Applewhite HL, Biocca FA (1992) A survey of position trackers. Presence Teleoper Virtual Environ 1(2):173–200
Moreira AHJ et al (2014) Electromagnetic tracker feasibility in the design of a dental superstructure for edentulous patients. In: IEEE MeMeA 2014—IEEE international symposium on medical measurements and applications, Proceedings, pp 1–6
Pao Y-H (1989) Adaptive pattern recognition and neural networks. Addison-Wesley Longman Publishing Co., Inc, Boston
Ramsamy P et al (2006) Using haptics to improve immersion in virtual environments. In: Alexandrov V et al (eds) Computational science—ICCS 2006 SE—81, Lecture Notes in Computer Science. Springer, Berlin, pp 603–609
Reinig K, Tracy R, Gilmore H, Mahalik T (1997) Some calibration information for a Phantom 1.5 a. In: Proceedings of the second PHANToM user’s group workshop. Dedham, Massachusetts, pp 70–73
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222
Srinivasan MA, Basdogan C (1997) Haptics in virtual environments: taxonomy, research status, and challenges. Comput Graph 21(4):393–404
Srinivasan MA (1995) Virtual reality: scientific and technical challenges. In: Durlach NI, Mavor AS (eds) Report of the committee on virtual reality research and development, National Research Council. National Academy Press, Washington, pp 161–187
Suykens JAK et al (2002) Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1–4):85–105
Tukey JW (1977) Schematic and summaries (pictures and numbers). In: Tukey JW (ed) Exploratory data analysis. Addison-Wesley Inc., pp 27–55
Vapnik V, Golowich SE, Smola AJ (1997) Support vector method for function approximation, regression estimation and signal processing. In: Mozer M, Jordan MI, Petsche T (eds) Advances in neural information processing systems 9—proceedings of the 1996 neural information processing systems conference (NIPS 1996). MIT Press, Cambridge, Dever, pp 281–287
Welch G, Foxlin E (2002) Motion tracking: no silver bullet, but a respectable arsenal. Comput Graph Appl IEEE 22(6):24–38
Xia P et al (2012) A new type haptics-based virtual environment system for assembly training of complex products. Int J Adv Manuf Technol 58(1):379–396
Yu H, Wilamowski BM (2011) Levenberg–Marquardt training. In: Industrial electronics handbook—intelligent systems, vol 5. CRC Press, Inc., pp 12–1–12–15
Zachmann G (1997) Distortion correction of magnetic fields for position tracking. In: Proceedings computer graphics international, pp 213–220, 251
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Frad, M., Maaref, H., Otmane, S. et al. A hybrid optical–mechanical calibration procedure for the Scalable-SPIDAR haptic device. Virtual Reality 21, 109–125 (2017). https://doi.org/10.1007/s10055-016-0303-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10055-016-0303-y