Efficient Panorama Database Indexing for Indoor Localization

Boin, Jean-Baptiste; Bobkov, Dmytro; Steinbach, Eckehard; Girod, Bernd

doi:10.1109/cbmi.2019.8877395

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Extending PoseNet, the authors in [33] introduced more sophisticated loss functions, albeit information of the scene geometry is needed. Other approaches involve indexing-based techniques using a collection of previously collected panoramas [34]. Presently, the state of the art combines cross-view matching between images of distinct domains (aerial, panoramas, and perspective), and it can offer even higher accuracy orientation estimates [35].…”

Section: Visual Self-localizationmentioning

confidence: 99%

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Dahlke,

Drakoulis,

Fernández García

et al. 2024

Sensors

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In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders’ positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

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Section: Visual Self-localizationmentioning

confidence: 99%

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Dahlke,

Drakoulis,

Fernández García

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Ref. [ 19 ] presented a coarse image retrieval, but the best-matched reference image cannot be found in this indexing scheme. Precise maps and a well-organized database contribute to improving the efficiency and accuracy of visual positioning.…”

Section: Related Workmentioning

confidence: 99%

A Visual Positioning Method of UAV in a Large-Scale Outdoor Environment

Zhao

et al. 2023

Sensors

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Visual positioning is a basic component for UAV operation. The structure-based methods are, widely applied in most literature, based on local feature matching between a query image that needs to be localized and a reference image with a known pose and feature points. However, the existing methods still struggle with the different illumination and seasonal changes. In outdoor regions, the feature points and descriptors are similar, and the number of mismatches will increase rapidly, leading to the visual positioning becoming unreliable. Moreover, with the database growing, the image retrieval and feature matching are time-consuming. Therefore, in this paper, we propose a novel hierarchical visual positioning method, which includes map construction, landmark matching and pose calculation. First, we combine brain-inspired mechanisms and landmarks to construct a cognitive map, which can make image retrieval efficient. Second, the graph neural network is utilized to learn the inner relations of the feature points. To improve matching accuracy, the network uses the semantic confidence in matching score calculations. Besides, the system can eliminate the mismatches by analyzing all the matching results in the same landmark. Finally, we calculate the pose by using a PnP solver. Furthermore, we evaluate both the matching algorithm and the visual positioning method experimentally in the simulation datasets, where the matching algorithm performs better in some scenes. The results demonstrate that the retrieval time can be shortened by three-thirds with an average positioning error of 10.8 m.

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“…Most researchers of visual localization focus on position estimation algorithms, such as the authors of [29,44,45], but few of them pay attention to image retrieval in the localization system, much less to off-line image database organization. A typical hierarchical indexing scheme is proposed in [46], but only coarse retrieval is presented, and the best-matched database image cannot be found by this scheme. A well-organized image database contributes to improving the accuracy and efficiency of a localization system.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

et al. 2022

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Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency.

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Efficient Panorama Database Indexing for Indoor Localization

Cited by 4 publications

References 21 publications

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

A Visual Positioning Method of UAV in a Large-Scale Outdoor Environment

Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization

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