LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment

• URL: http://arxiv.org/abs/2410.12269v1
• Date: Wed, 16 Oct 2024 06:09:27 GMT
• Title: LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment
• Authors: Juelin Zhu, Shen Yan, Long Wang, Shengyue Zhang, Yu Liu, Maojun Zhang,
• Abstract summary: We propose a new method for visual localization in complex 3D representations. Unlike existing localization algorithms, we estimate the pose of an Unmanned Vehicle (UAV) using a LevelDetail (LoD) 3D map.
• Score: 16.942854458136633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method named LoD-Loc for visual localization in the air. Unlike existing localization algorithms, LoD-Loc does not rely on complex 3D representations and can estimate the pose of an Unmanned Aerial Vehicle (UAV) using a Level-of-Detail (LoD) 3D map. LoD-Loc mainly achieves this goal by aligning the wireframe derived from the LoD projected model with that predicted by the neural network. Specifically, given a coarse pose provided by the UAV sensor, LoD-Loc hierarchically builds a cost volume for uniformly sampled pose hypotheses to describe pose probability distribution and select a pose with maximum probability. Each cost within this volume measures the degree of line alignment between projected and predicted wireframes. LoD-Loc also devises a 6-DoF pose optimization algorithm to refine the previous result with a differentiable Gaussian-Newton method. As no public dataset exists for the studied problem, we collect two datasets with map levels of LoD3.0 and LoD2.0, along with real RGB queries and ground-truth pose annotations. We benchmark our method and demonstrate that LoD-Loc achieves excellent performance, even surpassing current state-of-the-art methods that use textured 3D models for localization. The code and dataset are available at https://victorzoo.github.io/LoD-Loc.github.io/.

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