TY - GEN
T1 - Effects of Biped Humanoid Robot Walking Gaits on Sparse Visual Odometry Algorithms
AU - Shiguematsu, Yukitoshi Minami
AU - Brandao, Martim
AU - Hashimoto, Kenji
AU - Takanishi, Atsuo
N1 - Funding Information:
*This study was conducted as part of the Research Institute for Science and Engineering, Waseda University, and as part of the humanoid project at the Humanoid Robotics Institute, Waseda University. It was also supported in part by the Program for Leading Graduate Schools, the Graduate Program for Embodiment Informatics of the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT, Japan), by SolidWorks Japan K.K and Cybernet Systems Co.,Ltd. M. Brandao is funded by UK Research and Innovation and EPSRC, ORCA research hub (EP/R026173/1).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Motivated by experiments showing that humans regulate their walking speed in order to improve localization performance, in this paper we explore the effects of walking gait on biped humanoid localization. We focus on step length as a proxy for speed and because of its ready applicability to current footstep planners, and we compare the performance of three different sparse visual odometry (VO) algorithms as a function of step length: a direct, a semi-direct and an indirect algorithm. The direct algorithm's performance decreased the longer the step lengths, which along with the analysis of inertial and force/torque data, point to a decrease in performance due to an increase of mechanical vibrations. The indirect algorithm's performance decreased in an opposite way, i.e., showing more errors with shorter step lengths, which we show to be due to the effects of drift over time. The semi-direct algorithm showed a performance in-between the previous two. These observations show that footstep planning could be used to improve the performance of VO algorithms in the future.
AB - Motivated by experiments showing that humans regulate their walking speed in order to improve localization performance, in this paper we explore the effects of walking gait on biped humanoid localization. We focus on step length as a proxy for speed and because of its ready applicability to current footstep planners, and we compare the performance of three different sparse visual odometry (VO) algorithms as a function of step length: a direct, a semi-direct and an indirect algorithm. The direct algorithm's performance decreased the longer the step lengths, which along with the analysis of inertial and force/torque data, point to a decrease in performance due to an increase of mechanical vibrations. The indirect algorithm's performance decreased in an opposite way, i.e., showing more errors with shorter step lengths, which we show to be due to the effects of drift over time. The semi-direct algorithm showed a performance in-between the previous two. These observations show that footstep planning could be used to improve the performance of VO algorithms in the future.
KW - Ego-motion
KW - Humanoid robot
KW - Localization
KW - Visual odometry
KW - WABIAN-2R
UR - http://www.scopus.com/inward/record.url?scp=85062301835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062301835&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS.2018.8625015
DO - 10.1109/HUMANOIDS.2018.8625015
M3 - Conference contribution
AN - SCOPUS:85062301835
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 160
EP - 165
BT - 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
PB - IEEE Computer Society
T2 - 18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
Y2 - 6 November 2018 through 9 November 2018
ER -