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EgoCart: A Benchmark Dataset for Large-Scale Indoor image-based localization in Retail Stores

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第4期31卷 1253-1267页

作者： Spera, Emiliano Furnari, Antonino Battiato, Sebastiano Farinella, Giovanni Maria Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

We consider the task of localizing shopping carts in a retail store from egocentric images. Addressing this task allows to infer information on the behavior of the customers to understand how they move in the store and what they pay more attention to. To study the problem, we propose a large dataset of images collected in a real retail store. The dataset comprises 19, 531 RGB images along with depth maps, ground truth camera poses, as well as class labels specifying the areas of the store in which each image has been acquired. We release the dataset to the public to encourage research in large-scale image-based indoor localization and to address the scarcity of large datasets to tackle the problem. We hence perform a benchmark of several image-based localization techniques exploiting images and depth information on the proposed dataset. In our study, both localization performances and space/time requirements are compared. The results show that, while state-of-the-art approaches allow to achieve good results, there is space for improvement.

关键词： Cameras Pose estimation Robot vision systems Three-dimensional displays Benchmark testing Task analysis image retrieval Indoor localization image-based localization egocentric vision shopping cart localization

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Demo: image-based Indoor localization using Object Detection and LSTM 24

Demo: Image-based Indoor Localization using Object Detection...

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22nd Annual International Conference on Mobile Systems, Applications and Services (MOBISYS)

作者： Aoki, Yuki Kobayashi, Naoki Okoshi, Tadashi Nakazawa, Jin Keio Univ Tokyo Japan

ISBN: (纸本)9798400705816

In this work, we propose a novel model that focuses on object features by combining object detection with CNN and LSTM networks. In recent years, a multitude of deep learning-based methods for Visual localization, have been extensively researched. However, conventional methods do not adequately account for object-level features. Therefore, it is difficult to use indoors where similar objects appear frequently. Our method applies CNN for feature extraction on detected objects cropped by YOLOv8, an object detection algorithm, and then integrates these features into a single feature vector using LSTM, enabling location estimation that takes into account multiple object features. Experiments using the new indoor dataset of our laboratory room have revealed that our proposed method achieves a 19.0% higher accuracy compared to CNN models that input the whole image with the same number of layers. These results demonstrate the promising potential of exploring methods focused on object features for indoor localization. The codes are available at https://***/sakusaku3939/YoloLSTM.

关键词： Visual localization image-based localization Indoor localization Relocalization Absolute Pose Regression

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Automatic Visual Fingerprinting for Indoor image-based localization Applications

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第1期50卷 305-317页

作者： Vedadi, Farhang Valaee, Shahrokh Univ Toronto Dept Elect & Comp Engn Wireless & Internetworking Res Lab Toronto ON M5S 1A1 Canada

Indoor localization has been an active research area in the last decade. Various localization systems have been proposed based on different types of signals, including but not limited to WiFi, ultrawideband, inertial measurements, and visual signals. Fingerprinting-based methods are among the most popular methods due to their accuracy and ease of deployment. However, a disadvantage to fingerprinting-based methods is the training phase in which fingerprints have to be collected at known locations and stored for future localization inquiries. Recently a few methods have been proposed to alleviate this problem. In this paper, we focus on the possibility of reducing the burden of the training phase for visual (image-based) indoor localization systems by proposing a system that automatically generates the image-location database. The proposed system will be referred to as automatic visual fingerprinting that can be paired with any indoor image-based localization method. We will also validate the proposed system through extensive experiments.

关键词： Visualization Cameras Estimation Buildings Tracking Robustness Training Automatic visual fingerprinting (AVF) ego-motion estimation feature tracking image-based localization indoor localization

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Efficient image-based indoor localization with MEMS aid on the mobile device

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2022年第0期185卷 85-110页

作者： Shu, Mingcong Chen, Guoliang Zhang, Zhenghua China Univ Min & Technol Jiangsu Key Lab Resources & Environm Informat Eng Xuzhou 221116 Jiangsu Peoples R China

Recently, imaged-based localization has become a hotspot in indoor localization research due to its high accuracy and no additional infrastructure requirements. However, computational complexity and memory consumption are critical issues for terminal devices in natural large-scale indoor scenes. In this paper, we propose a system that takes advantage of built-in sensors, a microelectromechanical system (MEMS), and 3D model-based localization to overcome resource constraints. Furthermore, we propose a rough carrier six-degree-of-freedom (6-DoF) pose estimation and error analysis based on the pedestrian dead reckoning and attitude and heading reference systems (PDR/AHRS) with pedestrian motion constraints. We additionally recommend a geometric pose error propagation model to compute the 3D point pose uncertainty of corresponding 2D features on the query images and a matching pool to extract the activated points for determining the 2D-3D correspondence, which efficiently rejects the outlier through the geometric filter in the RANSAC loop of pose estimation. Moreover, based on the fusion of results from the MEMS-based and image-based localization, we can obtain an optimized carrier pose by using a dynamic reliability strategy to ensure the continuous robustness of the localization system and avoid failures when the matching lacks sufficient inliers. To evaluate the performance of the system, we conducted extensive experiments in large-scale indoor scenes. The results demonstrate that our image-based localization system generates excellent 2D-3D correspondence with the MEMS aid and that the proposed strategy obtains accurate 6DoF poses in almost real time by using a smartphone. When compared to the conventional straightforward 2D-3D method (ACS) for localization accuracy and usability tests in various indoor scenes, the proposed method can save time by more than 80% and reduce translation and rotation errors by 27% and 10%, respectively. We also show that our new localiz

关键词： image-based localization MEMS Geometric filter Pedestrian indoor positioning

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Change detection using weighted features for image-based localization

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ROBOTICS AND AUTONOMOUS SYSTEMS 2021年 135卷 103676-103676页

作者： Derner, Erik Gomez, Clara Hernandez, Alejandra C. Barber, Ramon Babuska, Robert Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Prague Czech Republic Carlos III Univ Madrid Dept Syst Engn & Automat Robot Lab Madrid Spain Delft Univ Technol Cognit Robot Delft Netherlands

Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled to achieve long-term robot autonomy. In vision-based localization and navigation methods, one of the major issues is the scene dynamics. The autonomous operation of the robot may become unreliable if the changes occurring in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects and other changes make many conventional methods fail. To deal with these challenges, we present a novel method for change detection based on weighted local visual features. The core idea of the algorithm is to distinguish the valuable information in stable regions of the scene from the potentially misleading information in the regions that are changing. We evaluate the change detection algorithm in a visual localization framework based on feature matching by performing a series of long-term localization experiments in various real-world environments. The results show that the change detection method yields an improvement in the localization accuracy, compared to the baseline method without change detection. In addition, an experimental evaluation on a public long-term localization data set with more than 10000 images reveals that the proposed method outperforms two alternative localization methods on images recorded several months after the initial mapping. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile robotics image-based localization Change detection Long-term autonomy

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On-Device Scalable image-based localization via Prioritized Cascade Search and Fast One-Many RANSAC

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IEEE TRANSACTIONS ON image PROCESSING 2019年第4期28卷 1675-1690页

作者： Ngoc-Trung Tran Dang-Khoa Le Tan Anh-Dzung Doan Thanh-Toan Do Tuan-Anh Bui Tan, Mengxuan Cheung, Ngai-Man Singapore Univ Technol & Design Singapore 487372 Singapore

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows and balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves the state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google street view image dataset show the potential of large-scale localization entirely on a typical mobile device.

关键词： image-based localization on-device localization image retrieval 2D-3D correspondence search hashing RANSAC

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A Novel image Retrieval Method for image based localization in Large-Scale Environment

A Novel Image Retrieval Method for Image Based Localization ...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Yin, Xiliang Ma, Lin Tan, Xuezhi Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Harbin Vocat & Tech Coll Harbin Peoples R China

ISBN: (纸本)9781728195070

Recently, indoor localization has become a hot research field. Although there are already different localization technologies, image-based localization is attracting more attention from researchers due to its unique advantages. The image retrieval algorithm is still a key part of the existing image-based localization system in a large scale environment. It is a challenging task for image retrieval to achieve a balance among image retrieval ratio, retrieval accuracy, and time complexity under such an environment. The state of the art image retrieval methods for image-based localization system still suffers from these problems. Therefore, we propose a novel image retrieval method aiming at reducing the time complexity without losing the retrieval accuracy. We compare the performance of the proposed method with the popular algorithms including the state of art in the indoor localization field, moreover, they are validated in a public dataset. It demonstrates that our method provides better results, which is verified by the well-known image recognition and visual localization algorithm.

关键词： Indoor localization image-based localization image Retrieval image Recognition Visual localization

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Long-term image-based vehicle localization improved with learnt semantic descriptors

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 35卷

作者： Cinaroglu, Ibrahim Bastanlar, Yalin Karamanoglu Mehmetbey Univ Dept Comp Engn TR-70100 Karaman Turkey Izmir Inst Technol Dept Comp Engn TR-35433 Izmir Turkey

Vision based solutions for the localization of vehicles have become popular recently. In this study, we employ an image retrieval based visual localization approach, in which database images are kept with GPS coordinates and the location of the retrieved database image serves as the position estimate of the query image in a city scale driving scenario. Regarding this approach, most existing studies only use descriptors extracted from RGB images and do not exploit semantic content. We show that localization can be improved via descriptors extracted from semantically segmented images, especially when the environment is subjected to severe illumination, seasonal or other long-term changes. We worked on two separate visual localization datasets, one of which (Malaga Streetview Challenge) has been generated by us and made publicly available. Following the extraction of semantic labels in images, we trained a CNN model for localization in a weakly-supervised fashion with triplet ranking loss. The optimized semantic descriptor can be used on its own for localization or preferably it can be used together with a state-ofthe-art RGB image based descriptor in hybrid fashion to improve accuracy. Our experiments reveal that the proposed hybrid method is able to increase the localization performance of the standard (RGB image based) approach up to 7.7% regarding Top-1 Recall values. (c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： image-based localization image matching Autonomous driving Semantic segmentation Semantic descriptor

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Improved image-based localization Using SFM and Modified Coordinate System Transfer

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第12期20卷 3298-3310页

作者： Salarian, Mandi Iliev, Nick Cetin, Ahmet Enis Ansari, Rashid Univ Illinois Dept Elect & Comp Engn Chicago IL 60608 USA Univ Illinois Chicago IL 60608 USA

Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database collected from social sharing websites like Flickr or services such as Google Street View. This paper proposes a new method for reliable estimation of the actual query camera location by optimally utilizing structure from motion (SFM) for three-dimensional (3-D) camera position reconstruction, and introducing a new approach for applying a linear transformation between two different 3-D Cartesian coordinate systems. Since the success of SFM hinges on effectively selecting among the multiple retrieved images, we propose an optimization framework to do this using the criterion of the highest intraclass similarity among images returned from retrieval pipeline to increase SFM convergence rate. The selected images along with the query are then used to reconstruct a 3-D scene and find the relative camera positions by employing SFM. In the last processing step, an effective camera coordinate transformation algorithm is introduced to estimate the query's geo-tag. The influence of the number of images involved in SFM on the ultimate position error is investigated by examining the use of three and four dataset images with different solution for calculating the query world coordinates. We have evaluated our proposed method on query images with known accurate ground truth. Experimental results are presented to demonstrate that our method outperforms other reported methods in terms of average error.

关键词： image-based localization BOF retrieval GPS uncertainty

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image-based localization Aided Indoor Pedestrian Trajectory Estimation Using Smartphones

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SENSORS 2018年第1期18卷 258页

作者： Zhou, Yan Zheng, Xianwei Chen, Ruizhi Xiong, Hanjiang Guo, Sheng Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Hubei Peoples R China

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.

关键词： image-based localization PDR low-cost indoor localization SfM

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