Effects of Walking Style and Symmetry on the Performance of Localization Algorithms for a Biped Humanoid Robot

Abstract

Motivated by experiments showing that humans' localization performance changes with walking parameters, in this paper we explore the effects of walking gait on biped humanoid localization. We focus on walking style (normal and gallop) and gait symmetry (one side slower), and we assess the performance of visual odometry (VO) and kinematic odometry algorithms for the robot's localization. Changing the walking style from normal to gallop slightly improved the performance of the visual localization, which was related to a reduction in torques on the feet. Changing the gait temporal symmetry worsened the performance of the visual algorithms, which according to an analysis of inertial data, is related to an increase of mechanical vibrations and camera rotations. Both changes of gait style and symmetry decreased the performance of the kinematic localization, caused by the increase of vertical ground reaction forces, to which kinematic odometry is very sensitive. These observations support our claim that gait and footstep planning could be used to improve the performance of localization algorithms in the future.