| Abstract | dc.description.abstract | In this article the classical self-localization approach is improved by
estimating, independently from the robot’s pose, the robot’s odometric error
and the landmarks’ poses. This allows using, in addition to fixed landmarks,
dynamic landmarks such as temporally local objects (mobile objects) and
spatially local objects (view-dependent objects or textures), for estimating the
odometric error, and therefore improving the robot’s localization. Moreover, the
estimation or tracking of the fixed-landmarks’ poses allows the robot to
accomplish successfully certain tasks, even when having high uncertainty in its
localization estimation (e.g. determining the goal position in a soccer
environment without directly seeing the goal and with high localization
uncertainty). Furthermore, the estimation of the fixed-landmarks’ pose allows
having global measures of the robot’s localization accuracy, by comparing the
real map, given by the real (a priori known) position of the fixed-landmarks,
with the estimated map, given by the estimated position of these landmarks.
Based on this new approach we propose an improved self-localization system
for AIBO robots playing in a RoboCup soccer environment, where the
odometric error estimation is implemented using Particle Filters, and the robot’s
and landmarks’ poses are estimated using Extended Kalman Filters. Preliminary
results of the system’s operation are presented. | es_CL |
