Sliding window bag-of-visual-words for low computational power robotics scene matching

In this paper, we introduce a new method, based on a sliding window geometrical extension to Bag-of-Visual-Words (called swBOVW) intended for application to low computational power robots. Benchmarked against RANSAC as a geometric validator to BOVW, three implementations of this technique are presented to improve either the performance or the computational cost. The three implementations are: as a replacement to RANSAC as a geometric validator; as a supplement to RANSAC; and as a replacement to traditional BOVW when the number of images in the database can be reduced. Seeking to utilise some of the geometric information ignored by traditional BOVW, this technique is developed from the use of sub-regions in Spatial Pyramids, and applied to the matching of whole images. This technique is applied in the context of humanoid robotic soccer to the problem of field end symmetry, and provides geometric validation along the horizontal axis of images. When applied, the technique has been able to either halve the cases of unresolved image queries, or halve the computational cost required to achieve comparable results to the benchmark.