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27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Donoser, Michael Schmalstieg, Dieter Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria

ISBN: (纸本)9781479951178

The prevalent approach to image-based localization is matching interest points detected in the query image to a sparse 3D point cloud representing the known world. The obtained correspondences are then used to recover a precise camera pose. The state-of-the-art in this field often ignores the availability of a set of 2D descriptors per 3D point, for example by representing each 3D point by only its centroid. In this paper we demonstrate that these sets contain useful information that can be exploited by formulating matching as a discriminative classification problem. Since memory demands and computational complexity are crucial in such a setup, we base our algorithm on the efficient and effective random fern principle. We propose an extension which projects features to fern-specific embedding spaces, which yields improved matching rates in short runtime. Experiments first show that our novel formulation provides improved matching performance in comparison to the standard nearest neighbor approach and that we outperform related randomization methods in our localization scenario.

关键词： Camera Pose Estimation Classification image-based localization

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Particle Filter based Tracking with image-based localization 20th

Particle Filter Based Tracking with Image-Based Localization

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20th International Conference on Knowledge - based and Intelligent Information and Engineering Systems (KES)

作者： Fukui, Shinji Hayakawa, Sou Iwahori, Yuji Nakamura, Tsuyoshi Bhuyan, M. K. Aichi Univ Educ Hirosawa 1 Kariya Aichi 4488542 Japan Chubu Univ Matsumoto Cho 1200 Kasugai Aichi 4878501 Japan Nagoya Inst Technol Showa Ku Gokiso Cho Nagoya Aichi 4668555 Japan Indian Inst Technol Gauhati 781039 Guwahati India

In this paper, we propose a new method for object tracking robust to the intersection with other objects with similar appearance and to the great rotation of the camera. The method uses 3D information of the feature points and the camera position by the image-based localization method. The movement information of the camera is used by the prediction process and the calculation process of the likelihood. Furthermore, the method extracts foreground objects by the homography transformation. The result is used by the likelihood function and by the process judging whether the target object is occluded by a background object or not. The proposed method can track the target object robustly when the camera rotates greatly and when the target object is occluded by the background object or the other object. Results are demonstrated by experiments using real videos. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of KES International

关键词： Tracking Particle Filter image-based localization Homography Transformation

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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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3D POINT CLOUD SIMPLIFICATION FOR image-based localization

3D POINT CLOUD SIMPLIFICATION FOR IMAGE-BASED LOCALIZATION

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IEEE International Conference on Multimedia & Expo Workshops (ICMEW)

作者： Cheng, Wentao Lin, Weisi Sun, Ming-Ting Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781479970797

Recent advances in image-based localization have made it possible to estimate the camera pose relative to 3D point clouds. In a multimedia cloud computing paradigm, large-scale 3D point clouds can be stored on the server side, and this enables us to implement image-based localization applications on mobile devices toward many cloud-media applications and services. When scaling the 3D point cloud database up to a world-wide level, the prohibitive memory footprint of such large-scale point clouds is a heavy burden to the server side. In this paper, we present a method to compute a compact subset of a 3D point cloud for image-based localization while achieving acceptable localization performance. In our method, we consider both coverage of database images and emphasis on 3D points of high spatial significance, and we show that our method outperforms the state-of-the-art methods on three public datasets especially when the size of the resultant subset is small. In essence, the priority of system resource is given to points of high spatial significance.

关键词： image-based localization point cloud simplification set cover multimedia cloud

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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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Particle Filter based Tracking with image-based localization

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Procedia Computer Science 2016年 96卷 977-986页

作者： Shinji Fukui Sou Hayakawa Yuji Iwahori Tsuyoshi Nakamura M.K. Bhuyan Aichi University of Education Hirosawa 1 Igaya Kariya 448-8542 Japan Chubu University Matsumoto-cho 1200 Kasugai 487-8501 Japan Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya 466-8555 Japan Indian Institute of Technology Guwahati Guwahati - 781039 India

关键词： Tracking Particle Filter image-based localization Homography Transformation

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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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A Survey of image-based Indoor localization using Deep Learning 22

A Survey of Image-Based Indoor Localization using Deep Learn...

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22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Bai, Xiaolan Huang, May Prasad, Neeli Rashmi Mihovska, Albena Dimitrova Int Technol Univ Elect & Comp Engn Departm Santa Clara CA 95050 USA Aarhus Univ Aarhus BSS Herring Denmark

ISBN: (纸本)9781728154190

The development of deep learning has rapidly updated image-based localization techniques. This paper presents a review and comparison of the current state-of-the-art methods for image-based localization using deep learning in the indoor environment. Traditional Global Structure from Motion (SfM) pipeline and learning-based pipeline from the recent techniques have been analyzed. based on the pipeline, the methods are categorized into three groups: learned features and matching, learned relative pose estimation, and learned absolute pose estimation. Since multiple sensors are used in many applications, sensor fusion techniques including image information, have been briefly reviewed in this paper as well. Furthermore, the paper discusses challenges in these methods and concludes learned features and matching is the more competitive method for indoor localization.

关键词： image-based localization indoor localization deep learning CNN SfM

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image based localization with Simulated Egocentric Navigations 14

Image Based Localization with Simulated Egocentric Navigatio...

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14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Orlando, Santi A. Furnari, Antonino Battiato, Sebastiano Farinella, Giovanni M. Univ Catania Dept Math & Comp Sci Catania Italy DWORD Xenia Progetti Srl Catania Italy

ISBN: (纸本)9789897583544

Current methods for image-based localization (IBL) require the collection and labelling of large datasets of images or videos for training purpose. Such procedure generally requires a significant effort, involving the use of dedicated sensors or the employment of structure from motion techniques. To overcome the difficulties of acquiring a dataset suitable to train models to study IBL, we propose a tool to generate simulated egocentric data starting from 3D models of real indoor environments. The generated data are automatically associated to the 3D camera pose information to be exploited during training, hence avoiding the need to perform "manual" labelling. To asses the effectiveness of the proposed tool, we generate and release a huge dataset of egocentric images using a 3D model of a real environment belonging to the S3DIS dataset. We also perform a benchmark study for 3 Degrees of Freedom (3DoF) indoor localization by considering an IBL pipeline based on image-retrieval and a triplet network. Results highlight that the generated dataset is useful to study the IBL problem and allows to perform experiments with different settings in a simple way.

关键词： image-based localization Similarity Search Metric Learning Simulated Data

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3D image-based Indoor localization Joint With WiFi Positioning 18

3D Image-based Indoor Localization Joint With WiFi Positioni...

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8th ACM International Conference on Multimedia Retrieval (ACM ICMR)

作者： Lu, Guoyu Song, Jingkuan Rochester Inst Technol Chester F Calson Ctr Imaging Sci Rochester NY 14623 USA Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450350464

We realize a system that utilizes WiFi to facilitate the image-based localization system, which avoids the confusion caused by the similar decoration inside the buildings. While WiFi-based localization thread obtains the rough location information, the image-based localization thread retrieves the best matching images and clusters the camera poses associated with the images into different location candidates. The image cluster closest to the WiFi localization outcome is selected for the exact camera pose estimation. The usage of WiFi significantly reduces the search scope, avoiding the extensive search of millions of descriptors in a 3D model. In the image-based localization stage, we also propose a novel 2D-to2D-to-3D localization framework which follows a coarse-to-fine strategy to quickly locate the query image in several location candidates and performs the local feature matching and camera pose estimation after choosing the correct image location by WiFi positioning. The entire system demonstrates significant benefits in combining both images and WiFi signals in localization tasks and great potential to be deployed in real applications.

关键词： image-based localization WiFi positioning 3D registration image retrieval Camera pose estimation

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