Embedded vision-based Monte-Carlo robot localisation without additional sensors

This paper presents a fast approach for vision-based self-localisation in the RoboCup middle size league without additional e.g. dead reckoning sensors. An omni-directional vision system extracts a few features from image that are mapped to an sparse a-priori known map of the environment using Monte Carlo filters. The Monte Carlo filters are also used to model a virtual odometry (mass-inertia model) which is maintained through the filter itself. The precision of approach is directly compared to a traditional approach using the identical data. We show that the approach is stable and reactive while keeping the processing time low.