A scalable visual localization pipeline that combines prior-guided reference candidate selection with on-the-fly local Structure-from-Motion reconstruction and PnP-based pose estimation is introduced, paving the way for 3D geospatial data acquisition using consumer devices and fully automated georeferencing approaches.
Abstract
Accurate and reliable pose information with respect to a reference frame is increasingly demanded across applications such as autonomous navigation, surveying, robotics, and augmented and mixed reality. Visual localization can serve as a complementary positioning modality to GNSS, whose applicability and accuracy are often limited. Yet, the accuracy potential of visual localization has not been systematically investigated against survey-grade demands. This is mainly due to the lack of publicly available, large-scale outdoor datasets with ground-truth poses in the sub-centimeter range. In this work, we address both gaps. We introduce a scalable visual localization pipeline that employs precisely georeferenced, high-resolution street-level imagery directly as the scene representation. It combines prior-guided reference candidate selection with on-the-fly local Structure-from-Motion reconstruction and PnP-based pose estimation. We further present the FHNW Muttenz dataset, a real-world dataset covering a contiguous 10 km street network mapped in two mobile mapping campaigns approximately 1.5 years apart. It consists of high-resolution reference imagery and query sequences acquired by four different cameras across five representative scenes. All images are precisely co-registered, yielding 6-DoF ground-truth poses in the sub-centimeter range. Using this dataset, we evaluate the accuracy potential of visual localization. Our experiments demonstrate median pose accuracies in the range of 1-5 cm for translation and 0.05-0.1{\deg} for rotation, reaching as low as 1 cm and 0.03{\deg} under favorable conditions. These results show that visual localization can complement survey-grade GNSS positioning, paving the way for 3D geospatial data acquisition using consumer devices and fully automated georeferencing approaches. The dataset is publicly available at: https://fhnw-muttenz-vl-dataset.github.io/.
DOU-Pose is proposed, a visual pose estimation framework built upon the Differentiable SAmple Consensus (DSAC)* pipeline to enhance the discriminative capability of scene coordinate regression through improved feature extraction and replaces standard convolutional layers with Depthwise Over-parameterized Convolution (DO-Conv).
Xin'an Qiu, Li-Wen Wang, Zezheng Dong et al.· Italian National Conference...· 0 citations
This work enhances the existing iterative object-basesd visual localization approach with an additional semantic feature derived from a pretrained semantic segmentation model and conducts a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs.
Yasmin Loeper, Markus Gerke, P. Fanta-Jende· The International Archives o...· 0 citations
Large-scale indoor mapping and positioning with vision sensors is fundamental to a wide range of applications, such as robotic navigation and augmented reality. However, the rapidly increasing number of detectable objects and the expanded spatial coverage jointly introduce matching ambiguity and high computational cost. Fine-grained object maps can improve accuracy but often accumulate redundant observations and slow down localization, whereas overly compressed scene representations may discard essential semantic and structural cues and degrade robustness. To balance accuracy and efficiency for indoor spatial sensing, we propose TS-MapLoc, a map-centric object-level localization framework based on cross-layer semantic co-mapping. It builds a lightweight topological–semantic map that integrates multi-scale information from the image layer and the object layer, reducing redundancy while preserving key structural constraints. On top of this map, a cognition-inspired progressive localization strategy performs coarse-to-fine inference via stage-wise filtering under cross-layer semantic consistency, effectively narrowing the search space and stabilizing matching. The proposed method supports efficient and accurate object-level localization for built-environment applications.
Cui-Yun Fang, Fan Wang, Ye-Dong Jiang et al.· IEEE Signal Processing Lette...· 0 citations
Simultaneous localization and mapping (SLAM) is one of the fundamental problems in robotics, as it enables autonomous operations in real-world scenarios. Under low illumination, reduced contrast, sensor noise, and motion blur degrade both feature extraction and feature matching, while compensating with LiDAR, depth, or thermal sensors raises cost, power draw, and integration complexity. Existing benchmarks remain dominated by well-lit indoor or daylight sequences, leaving open how far SLAM with standard RGB cameras can be pushed in the dark. We benchmark six systems spanning the feature-based, direct, filter-based, and learning-based paradigms - ORB-SLAM3, DSO, Kimera-VIO, OpenVINS, DPVO, and DPV-SLAM - on five LaMARia sequences of varying difficulty and illumination, reporting absolute and relative pose error alongside control-point recall. Kimera-VIO is the only system to track all five sequences to completion, combining the lowest relative pose error with steadily growing absolute error due to the absence of loop closure; DPVO and DPV-SLAM never lose tracking but incur absolute errors of roughly 100 m under low light; and the classical monocular pipelines (ORB-SLAM3, DSO) together with the filter-based OpenVINS fail outright or diverge on most of the harder and low-light sequences. The results suggest that RGB-only SLAM maintains stable low-light tracking only when both inertial fusion and global optimization are present. Closing the remaining gap will likely require low-light-specific learned front-ends or a return to complementary sensing.
Oleh Basystyi, Anna Stasyshyn, Oleksandr Kosovan et al.· arXiv.org· 0 citations
Accurate localization serves as an important component in autonomous driving systems. Traditional localization methods involve many standalone modules, which require complex hand-crafted rules and costly hyperparameter tuning by trial-and-error, therefore sacrificing the accuracy and generalization. In this paper, we propose an end-to-end visual localization approach, RAVE, in which the surrounding images are associated with the HD map data to estimate poses. To ensure high-quality observations for localization, a low-rank flow-based prior fusion module (FLORA) is developed to incorporate misaligned map prior into the perceived BEV features. Pursuing a balance among efficiency, interpretability, and accuracy, a hierarchical localization module is proposed, which efficiently estimates poses through a decoupled BEV neural matching-based pose solver (DEMA) using rasterized HD map, and then refines the estimation through a Transformer-based pose regressor (POET) using vectorized HD map. The experimental results demonstrate that our method can perform robust and accurate localization under varying environmental conditions while running efficiently.
Jinyu Miao, Yi He, Tuopu Wen et al.· Communications in Transporta...· 0 citations
Visual localization is a key technology in many vision-based measurement applications, aiming to estimate the camera pose of a query image in a known environment. However, most existing methods rely on heavy scene-specific representations, such as explicit 3-D map construction or per-scene training. Constructing and maintaining such representations introduces nonnegligible computational overhead, storage burden, and long-term maintenance costs. To address this issue, we propose a novel visual localization pipeline that uses a set of posed reference images as a lightweight scene representation and localizes query images without explicit 3-D map construction or scene-specific training. Specifically, we exploit a geometric foundation model to infer local multiview geometry from the query image and its retrieved references. Since the predicted geometry is expressed in an arbitrary local coordinate system with unknown scale, a key challenge is how to recover an accurate metric pose of the query image from such local predictions. To address this challenge, we design a global pose recovery strategy that first registers the predicted local geometry to the world coordinate system through joint center–orientation similarity alignment using the posed reference images as global anchors, and then refines the query pose by optimizing query-associated 3-D landmarks under multiview 2-D–3-D geometric constraints. The experimental results on multiple benchmark datasets show that our method achieves competitive localization performance and improved robustness under sparse reference-view settings and challenging viewpoint or appearance variations, reducing the average translation and rotation errors of the strongest Unseen baseline from 55 cm and 0.56° to 13 cm and 0.23° on Cambridge Landmarks, respectively.
Wenhao Lin, Cong Guo, Yu Wu et al.· IEEE Transactions on Instrum...· 0 citations
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