N. Kwak,
K. Yokoi,
O. Stasse,
T. Foissotte,
IEEE/RAS Int. Conf. on Humanoid Robotics (ICHR),
2009,
Publisher
Bib
Abstract:
Necessity to recognize the world like a home environment by a humanoid robot has recently been arisen for daily usages. As an observation sensor, stereo vision is the most common device for a humanoid robot to obtain the environmental data, but it is more erroneous than a laser sensor. To overcome the inaccuracy of stereo vision, we propose a particle-based SLAM technique so that the SLAM posterior is estimated by multiple hypotheses. The major difficulty of the particle-based SLAM with 3D grid maps is the high computational cost. To reduce the computational cost, we also propose a scheduling method for the time when to match and for particles that engage in the matching process. Through experiments with a humanoid robot, HRP-2, it is shown that the proposed approach can reduce the computational cost while preserving estimation accuracy.