VILSS: 2D Pairwise Geometry for Robust and Scalable Place Recognition

Edward Jones, Dyson Research Lab, Imperial College London

In this talk, I will present an overview of my PhD research on extending recent trends in visual place recognition to offer robustness and scalability. The underlying theme of my work is the exploitation of 2D geometry between pairs of local image features, which is often overlooked in favour of stronger 3D constraints. I will show how 2D geometry can be effectively applied to complement the limitations of 3D geometry, or even replace it at a fraction of the computational cost. The talk is divided into three sections, each discussing one example of such a method. First, I will show how 2D pairwise geometry can help to eliminate false positive feature correspondences which arise from RANSAC-based 3D geometric constraints. Then, an inverted index consisting of pairwise geometries will be introduced, which makes scalable recognition with geometry possible. Finally, I will introduce a topological robot localisation system which aims towards encoding probability into place recognition attempts, and hence offering suitability to visual SLAM frameworks.