GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models

• URL: http://arxiv.org/abs/2006.13670v2
• Date: Mon, 5 Oct 2020 11:07:53 GMT
• Title: GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models
• Authors: Huaiyang Huang, Haoyang Ye, Yuxiang Sun, Ming Liu
• Abstract summary: We present a method that tracks a camera in a prior map modelled by the Gaussian Mixture Model (GMM) With the pose estimated by the front-end initially, the local visual observations and map components are associated efficiently. We show how our system can provide a centimeter-level localization accuracy with only trivial computational overhead.
• Score: 23.72910988500612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incorporating prior structure information into the visual state estimation could generally improve the localization performance. In this letter, we aim to address the paradox between accuracy and efficiency in coupling visual factors with structure constraints. To this end, we present a cross-modality method that tracks a camera in a prior map modelled by the Gaussian Mixture Model (GMM). With the pose estimated by the front-end initially, the local visual observations and map components are associated efficiently, and the visual structure from the triangulation is refined simultaneously. By introducing the hybrid structure factors into the joint optimization, the camera poses are bundle-adjusted with the local visual structure. By evaluating our complete system, namely GMMLoc, on the public dataset, we show how our system can provide a centimeter-level localization accuracy with only trivial computational overhead. In addition, the comparative studies with the state-of-the-art vision-dominant state estimators demonstrate the competitive performance of our method.

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