Section: New Results

Experimental evaluation of attitude estimation algorithms for smartphones

  • Context: Pervasive applications on smartphones increasingly rely on techniques for estimating attitude. Attitude is the orientation of the smartphone with respect to Earth’s local frame.

    Modern smartphones embed sensors such as accelerometer, gyroscope, and magnetometer which make it possible to leverage existing attitude estimation algorithms.

  • Contribution: we focused on smartphone attitude estimation. We proposed the first benchmark using a motion lab with a high precision (the Inria Kinovis platform) for the purpose of comparing and evaluating filters from the literature on a common basis. This allowed us to provide the first in-depth comparative analysis in this context. In particular, we focused on typical motions of smartphones when carried by pedestrians. Furthermore, we proposed a new parallel filtering technique for limiting the impact of magnetic perturbations with any attitude estimation algorithm used in this context. We showed how our technique compares and improves over previous works. We made our benchmark available (see Benchmarks Attitude Smartphones in Software section) and payed attention to the reproducibility of results. We analyzed and discussed the obtained results and reported on lessons learned [14], [9], [24].