CyberLoc: Towards Accurarte Long-Term Visual Localization
Our approach wins the localization challenge of ECCV 2022 workshop on Map-based Localization for Autonomous Driving (MLAD-ECCV2022). Especially, our approach achieves 98.1% on the highest accuracy level, well over 50% ahead of the second place team.
Presented a robust and accurate mapping and localization pipeline, applied a pose graph optimization method combining absolute pose from global visual localization and relative pose from slam, designed consensus set maximization to utilize multi-session map and select the best pose track.
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