We propose a novel method for aerial visual localization over low Level-of-Detail (LoD) city models. Previous wireframe-alignment-based method LoD-Loc has shown promising localization results leveraging LoD models. However, LoD-Loc mainly relies on high-LoD (LoD3 or LoD2) city models, but the majority of available models and those many countries plan to construct nationwide are low-LoD (LoD1). Consequently, enabling localization on low-LoD city models could unlock drones' potential for global urban localization. To address these issues, we introduce LoD-Loc v2, which employs a coarse-to-fine strategy using explicit silhouette alignment to achieve accurate localization over low-LoD city models in the air. Specifically, given a query image, LoD-Loc v2 first applies a building segmentation network to shape building silhouettes. Then, in the coarse pose selection stage, we construct a pose cost volume by uniformly sampling pose hypotheses around a prior pose to represent the pose probability distribution. Each cost of the volume measures the degree of alignment between the projected and predicted silhouettes. We select the pose with maximum value as the coarse pose. In the fine pose estimation stage, a particle filtering method incorporating a multi-beam tracking approach is used to efficiently explore the hypothesis space and obtain the final pose estimation. To further facilitate research in this field, we release two datasets with LoD1 city models covering 10.7 km , along with real RGB queries and ground-truth pose annotations. Experimental results show that LoD-Loc v2 improves estimation accuracy with high-LoD models and enables localization with low-LoD models for the first time. Moreover, it outperforms state-of-the-art baselines by large margins, even surpassing texture-model-based methods, and broadens the convergence basin to accommodate larger prior errors.

Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datasets. Current datasets often focus on small-scale scenes and lack viewpoint variability, accurate ground truth (GT) pose, and UAV build-in sensor data. To address these limitations, we introduce a large-scale 6-DoF UAV dataset for localization (UAVD4L) and develop a two-stage 6-DoF localization pipeline (UAVLoc), which consists of offline synthetic data generation and online visual localization. Additionally, based on the 6DoF estimator, we design a hierarchical system for tracking ground target in $3 D$ space. Experimental results on the new dataset demonstrate the effectiveness of the proposed approach. Code and dataset are available at https://github.com/RingoWRW/UAVD4L.

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 will be made available upon publication.

Abstract Despite the impressive progress in visual localisation, 6‐DoF cross‐view localisation is still a challenging task in the computer vision community due to the huge appearance changes. To address this issue, the authors propose the CLaSP, a coarse‐to‐fine framework, which leverages a synthetic panorama to facilitate cross‐view 6‐DoF localisation in a large‐scale scene. The authors first leverage a segmentation map to correct the prior pose, followed by a synthetic panorama on the ground to enable coarse pose estimation combined with a template matching method. The authors finally formulate the refine localisation process as feature matching and pose refinement to obtain the final result. The authors evaluate the performance of the CLaSP and several state‐of‐the‐art baselines on the Airloc dataset, which demonstrates the effectiveness of our proposed framework.

While visual localization has made significant advances in recent years, it still lacks robustness in low-light situations. Thermal camera images, which capture temperature data, provide a potential solution for these environments. However, the scarcity of well-annotated, publicly available datasets for thermal localization, particularly those focused on absolute pose estimation, impedes further advancement in this field. In this research, we introduce a novel dataset that includes six-degree-of-freedom (6-DoF) absolute poses of query images for large-scale, realistic aerial localization of thermal images. Besides, we introduce a render-to-localization pipeline tailored for thermal image localization. This pipeline predicts the 6-DoF pose of a query using a synthetic technique based on geometric refined thermal model. Experimental results demonstrate the effectiveness of our method on this newly proposed dataset. Notably, our method achieves a median position error of less than 1.5 m and a median angle error of less than 1.5° under diverse test conditions. A comprehensive analysis of factors influencing localization accuracy is also provided. Our code and dataset will be available athttps://github.com/RingoWRW/ATLoc.

Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks. Compared with aerial oblique photography capture, ground-level map collection lacks scalability and complete coverage. In this work, we propose to go beyond the traditional ground-level setting and exploit cross-view 6-DoF localization from aerial to ground. We address this problem by formulating camera pose estimation as an iterative render-and-compare pipeline and enhancing the algorithm robustness through augmenting seeds from noisy initial priors. As no public dataset exists for the studied problem, we have collected a new dataset that provides a variety of cross-view images from smartphones and low-altitude drones and developed a semi-automatic system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate that our method outperforms other approaches by a large margin. Code is available at https://github.com/Choyaa/Render2Loc.