CLaSP: Cross-view 6-DoF localisation assisted by synthetic panorama

Abstract

Despite the impressive progress in visual localisation, 6-DoF cross-view localisation is still a challenging task in the computer vision community due to the huge appearance changes. To address this issue, the authors propose the CLaSP, a coarse-to-fine framework, which leverages a synthetic panorama to facilitate cross-view 6-DoF localisation in a large-scale scene. The authors first leverage a segmentation map to correct the prior pose, followed by a synthetic panorama on the ground to enable coarse pose estimation combined with a template matching method. The authors finally formulate the refine localisation process as feature matching and pose refinement to obtain the final result. The authors evaluate the performance of the CLaSP and several state-of-the-art baselines on the Airloc dataset, which demonstrates the effectiveness of our proposed framework.