This technical report introduces CyberLoc, an image-based visual localization
pipeline for robust and accurate long-term pose estimation under challenging
conditions. The proposed method comprises four modules connected in a sequence.
First, a mapping module is applied to build accurate 3D maps of the scene, one
map for each reference sequence if there exist multiple reference sequences
under different conditions. Second, a single-image-based localization pipeline
(retrieval--matching--PnP) is performed to estimate 6-DoF camera poses for each
query image, one for each 3D map. Third, a consensus set maximization module is
proposed to filter out outlier 6-DoF camera poses, and outputs one 6-DoF camera
pose for a query. Finally, a robust pose refinement module is proposed to
optimize 6-DoF query poses, taking candidate global 6-DoF camera poses and
their corresponding global 2D-3D matches, sparse 2D-2D feature matches between
consecutive query images and SLAM poses of the query sequence as input.
Experiments on the 4seasons dataset show that our method achieves high accuracy
and robustness. In particular, our approach wins the localization challenge of
ECCV 2022 workshop on Map-based Localization for Autonomous Driving
(MLAD-ECCV2022).

CyberLoc 是一种图像定位管道，可在困难条件下实现稳健且准确的长期位姿估计；该方法由四个模块组成，包括地图构建、基于单个图像的定位、一致性最大化、鲁棒姿态优化，实验结果表明该方法能够在 Map-based Localization for Autonomous Driving 中获胜