Key-frame Selection for Multi-robot Simultaneous Localization and Tracking in Robot Soccer Field

Abstract

Optical images provide rich features but require extensive computation resources to process for SLAM. When there are limited computation resources on the robots, it becomes a heavy burden to process the images in real-time. This paper presents the design and implementation of key-frame selection algorithm for multiple robots simultaneous localization and tracking on the multi-robot soccer games which have pre-defined field and objects. Compared to traditional key-frame selection algorithms, this work makes use of the temporal and spatial relationship among objects on the pre-defined field to compute the information entropy. The selection ratio can be adjusted by two parameters: entropy threshold and the maximum moving distance. The experimental results show that the developed method can effectively detect the change of scene using selected key-frames. And comparing with the localization results using all the images, using less than 20% of all images after walking 11,203mm it only increase up to 0.87% trajectory errors.