VVLoc: Prior-free 3-DoF Vehicle Visual Localization

• URL: http://arxiv.org/abs/2602.00810v1
• Date: Sat, 31 Jan 2026 16:37:30 GMT
• Title: VVLoc: Prior-free 3-DoF Vehicle Visual Localization
• Authors: Ze Huang, Zhongyang Xiao, Mingliang Song, Longan Yang, Hongyuan Yuan, Li Sun,
• Abstract summary: We propose a unified pipeline that employs a single neural network to concurrently achieve topological and metric vehicle localization using multi-camera system.<n> VVLoc first evaluates the geo-proximity between visual observations, then estimates their relative metric poses using a matching strategy, while also providing a confidence measure.<n>We evaluate VVLoc not only on the publicly available datasets, but also on a more challenging self-collected dataset.
• Score: 6.151313455860856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Localization is a critical technology in autonomous driving, encompassing both topological localization, which identifies the most similar map keyframe to the current observation, and metric localization, which provides precise spatial coordinates. Conventional methods typically address these tasks independently, rely on single-camera setups, and often require additional 3D semantic or pose priors, while lacking mechanisms to quantify the confidence of localization results, making them less feasible for real industrial applications. In this paper, we propose VVLoc, a unified pipeline that employs a single neural network to concurrently achieve topological and metric vehicle localization using multi-camera system. VVLoc first evaluates the geo-proximity between visual observations, then estimates their relative metric poses using a matching strategy, while also providing a confidence measure. Additionally, the training process for VVLoc is highly efficient, requiring only pairs of visual data and corresponding ground-truth poses, eliminating the need for complex supplementary data. We evaluate VVLoc not only on the publicly available datasets, but also on a more challenging self-collected dataset, demonstrating its ability to deliver state-of-the-art localization accuracy across a wide range of localization tasks.

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