As the robot captures new images, they are encoded as a vector in topic space and organised in a similarity topic graph using the star clustering algorithm. Images in a cluster possess similar visual topics, and cluster centres accurately capture the visual theme in the associated images(see figure opposite).
Someday, mobile robots will operate continually. Day after day, they will be in receipt of a never ending stream of images. In anticipation of this, this paper is about having a mobile robot generate apt and compact summaries of its life experience. We consider a robot moving around its environment both revisiting and exploring, accruing images as it goes. We describe how we can choose a subset of images which captures the essence of the robot’s visual experience – illustrating both what was ordinary and what was extraordinary – and which summarises the robot’s cumulative visual experience. Moreover we show how to do this such that the time cost of generating a summary is largely independent of the total number of images processed. No one day is harder to summarise than any other.