Experimental evaluation of visual place recognition algorithms for personal indoor localization

Nowicki, Michał; Wietrzykowski, Jan; Skrzypczyński, Piotr

doi:10.1109/ipin.2016.7743649

Cited by 6 publications

(11 citation statements)

References 20 publications

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“…In this article we significantly extend our preliminary research, presented in the recent conference paper [30]. This research revealed that the ABLE-M algorithm based on image sequences is particularly beneficial for place recognition in corridor-like environments, where not enough salient features are available for reliable comparison based on a single query image.…”

Section: Contributionsupporting

confidence: 57%

“…The OpenFABMAP system has one major parameter to tune, which is the probability threshold t p . In the preliminary tests [30], we couldn't find a single setting of t p that would allow achieving no false positives with the satisfactory number of correct recognitions. Therefore, we decided to modify the OpenFABMAP operation to include information about a sequence of images.…”

Section: Modifications To Openfabmapmentioning

confidence: 98%

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Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Nowicki

Wietrzykowski

Skrzypczyński

2017

Wireless Pers Commun

Self Cite

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The paper presents an approach to indoor personal localization on a mobile device based on visual place recognition. We implemented on a smartphone two state-ofthe-art algorithms that are representative to two different approaches to visual place recognition: FAB-MAP that recognizes places using individual images and ABLE-M that utilizes sequences of images. These algorithms are evaluated in environments of different structure, focusing on problems commonly encountered when a mobile device camera is used. The conclusions drawn from this evaluation are guidelines to design the FastABLE system, which is based on the ABLE-M algorithm but introduces major modifications to the concept of image matching. The improvements radically cut down the processing time and improve scalability, making it possible to localize the user in long image sequences with the limited computing power of a mobile device. The resulting place recognition system compares favorably to both the ABLE-M and the FAB-MAP solutions in the context of real-time personal localization.

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Section: Contributionsupporting

confidence: 57%

Section: Modifications To Openfabmapmentioning

confidence: 98%

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Nowicki

Wietrzykowski

Skrzypczyński

2017

Wireless Pers Commun

Self Cite

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show abstract

“…Similar to the SeqSLAM method, which uses the image sequence, ABLE-M [ 34 ] deploys a binary description of images that reduces memory and computational costs, remaining stable despite environmental changes that affect image appearance. Nowicki et al [ 35 ] evaluated the application of single image and image sequence localization in indoor environments on mobile devices, validating the feasibility and real-time performance of both of these algorithms. Furthermore, Nowicki et al also found that algorithms using single images are not susceptible to local self-similarity issues inside buildings, as texture changes in images are not as large as those images taken in outdoor space.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones

Zhou

Zheng

Chen

et al. 2018

Sensors

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Accurately determining pedestrian location in indoor environments using consumer smartphones is a significant step in the development of ubiquitous localization services. Many different map-matching methods have been combined with pedestrian dead reckoning (PDR) to achieve low-cost and bias-free pedestrian tracking. However, this works only in areas with dense map constraints and the error accumulates in open areas. In order to achieve reliable localization without map constraints, an improved image-based localization aided pedestrian trajectory estimation method is proposed in this paper. The image-based localization recovers the pose of the camera from the 2D-3D correspondences between the 2D image positions and the 3D points of the scene model, previously reconstructed by a structure-from-motion (SfM) pipeline. This enables us to determine the initial location and eliminate the accumulative error of PDR when an image is successfully registered. However, the image is not always registered since the traditional 2D-to-3D matching rejects more and more correct matches when the scene becomes large. We thus adopt a robust image registration strategy that recovers initially unregistered images by integrating 3D-to-2D search. In the process, the visibility and co-visibility information is adopted to improve the efficiency when searching for the correspondences from both sides. The performance of the proposed method was evaluated through several experiments and the results demonstrate that it can offer highly acceptable pedestrian localization results in long-term tracking, with an error of only 0.56 m, without the need for dedicated infrastructures.

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“…We define visual location estimation as the process of estimating the geographical coordinates of a scene based solely on the visual cues existing in the given image. One could think multiple variations of this task, depending on whether we restrict our input to a particular kind of scenes, e.g., landmarks [3,5,46], to be from a particular area [1,17,44], or on whether we are using different inputs, e.g., a sequence of images per scene [2,31,32], or aerial imagery [24,34,43]. In this study, we focus on global-scale location estimation from single images, which is the most challenging problem setting.…”

Section: Introductionmentioning

confidence: 99%

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

Kordopatis-Zilos¹,

Galopoulos²,

Papadopoulos³

et al. 2021

Preprint

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In this paper, we address the problem of global-scale image geolocation, proposing a mixed classification-retrieval scheme. Unlike other methods that strictly tackle the problem as a classification or retrieval task, we combine the two practices in a unified solution leveraging the advantages of each approach with two different modules. The first leverages the EfficientNet architecture to assign images to a specific geographic cell in a robust way. The second introduces a new residual architecture that is trained with contrastive learning to map input images to an embedding space that minimizes the pairwise geodesic distance of same-location images. For the final location estimation, the two modules are combined with a search-within-cell scheme, where the locations of most similar images from the predicted geographic cell are aggregated based on a spatial clustering scheme. Our approach demonstrates very competitive performance on four public datasets, achieving new state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km range on Im2GPS3k.

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Experimental evaluation of visual place recognition algorithms for personal indoor localization

Cited by 6 publications

References 20 publications

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

Image-Based Localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones

Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

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