Map-assisted visual localization using line features in urban area

A novel method is presented for robustly estimating the location of a mobile robot in urban areas based on images extracted from a monocular onboard camera, given a 2D building boundary map. The proposed approach firstly reconstructs a set of vertical planes by sampling and clustering vertical lines from the image with Random Sample Consensus (RANSAC), using the derived 1D homographies to inform the planar model. Then, an optimal autonomous localization algorithm based on the 2D building outline map is proposed. The physical experiments are carried out to validate the robustness and accuracy of our localization approach.