Abstract
With modern cycling computers it is possible to provide cyclists with complex feedback during rides. If the feedback is course-dependent, it is necessary to know the riders current position on the course. Different approaches to estimate the position on the course from common GPS and speed sensors were compared: the direct distance measure derived from the number of rotations of the wheel, GPS coordinates projected onto the course trajectory, and a Kalman filter incorporating speed as well as GPS measurements. To quantify the accuracy of the different methods, an experiment was conducted on a race track where a fixed point on the course was tagged during the ride. The Kalman filter approach was able to overcome certain shortcomings of the other two approaches and achieved a mean error of \({-0.13}\,{\text {m}}\) and a root mean square error of \({0.97}\,{\text {m}}\).
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Wolf, S., Dobiasch, M., Gonzalez, A.A., Saupe, D. (2018). How to Accurately Determine the Position on a Known Course in Road Cycling. In: Lames, M., Saupe, D., Wiemeyer, J. (eds) Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017). IACSS 2017. Advances in Intelligent Systems and Computing, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-67846-7_11
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DOI: https://doi.org/10.1007/978-3-319-67846-7_11
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