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Are Large-Scale 3D Models Really Necessary for Accurate Visual localization?

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第3期43卷 814-829页

作者： Torii, Akihiko Taira, Hajime Sivic, Josef Pollefeys, Marc Okutomi, Masatoshi Pajdla, Tomas Sattler, Torsten Tokyo Inst Technol Sch Engn Dept Syst & Control Engn Tokyo 1528550 Japan PSL Res Univ Ecole Normale Super Dept Informat INRIAWILLOW ProjectENSCNRSUMR 8548 F-75006 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16000 Czech Republic Swiss Fed Inst Technol Dept Comp Sciene CH-8092 Zurich Switzerland Microsoft CH-8304 Wallisellen Switzerland Chalmers Univ Technol S-41296 Gothenburg Sweden

Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6 degree-of-freedom (DOF) pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a higher pose accuracy compared to state-of-the-art structure-based methods, albeight at higher run-time costs. We show that some of these run-time costs can be alleviated by exploiting known database image poses. Our results suggest that we might want to reconsider the need for large-scale 3D models in favor of more local models, but also that further research is necessary to accelerate the local reconstruction process.

关键词： Three-dimensional displays Solid modeling Visualization Cameras Two dimensional displays Databases Pose estimation Visual localization image-based localization place recognition pose estimation image retrieval

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Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging

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NEUROCOMPUTING 2016年第Part1期173卷 83-92页

作者： Lu, Guoyu Yan, Yan Ren, Li Saponaro, Philip Sebe, Nicu Kambhamettu, Chandra Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Trento Dept Informat Engn & Comp Sci Trento Italy

Indoor localization is one of the key problems in robotics research. Most current localization systems use cellular base stations and Wifi signals, whose localization accuracy is largely dependent on the signal strength and is sensitive to environmental changes. With the development of camera-based technologies, image-based localization may be employed in an indoor environment where the GPS signal is weak. Most of the existing image-based localization systems are based on color images captured by cameras, but this is only feasible in environments with adequate lighting conditions. In this paper, we introduce an image-based localization system based on thermal imaging to make the system independent of light sources, which are especially useful during emergencies such as a sudden power outage in a building. As thermal images are not obtained as easily as color images, we apply active transfer learning to enrich the thermal image classification learning, where normal RGB images are treated as the source domain, and thermal images are the target domain. The application of active transfer learning avoids random target training sample selection and chooses the most informative samples in the learning process. Through the proposed active transfer learning, the query thermal images can be accurately used to indicate the location. Experiments show that our system can be efficiently deployed to perform indoor localization in a dark environment. (C) 2015 Elsevier B.V. All rights reserved.

关键词： image-based localization Active transfer learning Thermal imaging

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A Two-Stage Outlier Filtering Framework for City-Scale localization Using 3D SfM Point Clouds

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IEEE TRANSACTIONS ON image PROCESSING 2019年第10期28卷 4857-4869页

作者： Cheng, Wentao Chen, Kan Lin, Weisi Goesele, Michael Zhang, Xinfeng Zhang, Yabin Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Nanyang Technol Univ Fraunhofer Singapore Singapore 639798 Singapore Tech Univ Darmstadt Dept Comp Sci D-64283 Darmstadt Germany Univ Chinese Acad Sci Dept Comp Sci & Technol Beijing 100049 Peoples R China Tencent Median Lab Shenzhen 518057 Peoples R China

Three-dimensional structure-based localization aims to estimate the six-DOF camera pose of a query image by means of feature matches against a 3D Structure-from-Motion (SfM) point cloud. For city-scale SfM point clouds with tens of millions of points, it becomes more and more difficult to disambiguate matches. Therefore, a 3D structure-based localization method, which can efficiently handle matches with very large outlier ratios, is needed. We propose a two-stage outlier filtering framework for city-scale localization that leverages both visibility and geometry intrinsics of the SfM point clouds. First, we propose a visibility-based outlier filter, which is based on a bipartite visibility graph, to filter outliers on a coarse level. Second, we apply a geometry-based outlier filter to generate a set of fine-grained matches with a novel data-driven geometrical constraint for efficient inlier evaluation. The proposed two-stage outlier filtering framework only relies on the intrinsic information of the SfM point cloud. It is thus widely applicable to be embedded into the existing localization approaches. The experimental results on two real-world datasets demonstrate the effectiveness of the proposed two-stage outlier filtering framework for city-scale localization.

关键词： City-scale localization outlier filter image-based localization hybrid inlier evaluation

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Global localization from Monocular SLAM on a Mobile Phone

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2014年第4期20卷 531-539页

作者： Ventura, Jonathan Arth, Clemens Reitmayr, Gerhard Schmalstieg, Dieter Graz Univ Technol A-8010 Graz Austria

We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client. The localization result is updated each time a keyframe is added, and observations of global anchor points are added to the client-side bundle adjustment process to further refine the SLAM map registration and limit drift. The end result is a 6DoF tracking and mapping system which provides globally registered tracking in real-time on a mobile device, overcomes the difficulties of localization with a narrow field-of-view mobile phone camera, and is not limited to tracking only in areas covered by the offline reconstruction.

关键词： image-based localization monocular SLAM real-time tracking global positioning mobile augmented reality

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An Efficient Indoor localization based on Deep Attention Learning Model

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Computer Systems Science & Engineering 2023年第8期46卷 2637-2650页

作者： Amr Abozeid Ahmed I.Taloba Rasha M.Abd El-Aziz Alhanoof Faiz Alwaghid Mostafa Salem Ahmed Elhadad Department of Computer Science College of Science and ArtsJouf UniversityQurayyatSaudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityEgypt Computer Science Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service *** a result,it is crucial to look for low-cost methods that can provide exact localization in indoor *** this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an ***-based localization faces many issues,such as image scale and rotation ***,image-based localization’s accuracy and speed(latency)are two critical *** paper proposes an efficient 6-DoF deep-learning model for image-based *** model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time *** complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and *** model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance ***,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome *** on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and *** the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the ***,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the *** to the findings,the proposed approach is superior and more successful than the baseline methods.

关键词： image-based localization computer vision deep learning attention module VGG-16

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A Comparison of Visual Navigation Approaches based on localization and Reinforcement Learning in Virtual and Real Environments 15

A Comparison of Visual Navigation Approaches based on Locali...

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15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Rosano, Marco Furnari, Antonino Gulino, Luigi Farinella, Giovanni Maria Univ Catania Catania Italy OrangeDev Srl Via Vasco de Gama 91 Florence Italy

ISBN: (纸本)9789897584022

Visual navigation algorithms allow a mobile agent to sense the environment and autonomously find its way to reach a target (e.g. an object in the environment). While many recent approaches tackled this task using reinforcement learning, which neglects any prior knowledge about the environments, more classic approaches strongly rely on self-localization and path planning. In this study, we compare the performance of single-target and multi-target visual navigation approaches based on the reinforcement learning paradigm, and simple baselines which rely on image-based localization. Experiments performed on discrete-state environments of different sizes, comprised of both real and virtual images, show that the two paradigms tend to achieve complementary results, hence suggesting that a combination of the two approaches to visual navigation may be beneficial.

关键词： Visual Navigation Reinforcement Learning image-based localization First Person Vision

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Visual Landmark-based localization for MAVs Using Incremental Feature Updates

Visual Landmark-based Localization for MAVs Using Incrementa...

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2nd Joint 3DIM/3DPVT International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Wendel, Andreas Maurer, Michael Bischof, Horst Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria

ISBN: (纸本)9780769548739

Micro aerial vehicles (MAVs) are gaining importance as image acquisition tools in urban environments, where areas of interest are often close to buildings and to the ground. While GPS is still the most widely used sensor for outdoor localization, urban applications motivate the change towards visual localization. We present a framework based on metric, geo-referenced visual landmarks, which can be obtained by taking images with a consumer camera at ground level. Visual landmarks serve as prior knowledge to the MAV and allow robust, high-accuracy localization in urban environments. The issue of differing camera views in higher altitudes is reduced by incremental feature updates, a novel technique which boosts the performance by 30% in comparison to previous work, facilitates long-term operation, and results in a localization rate of 83%. We validate the visual pose estimation in-flight by comparison to IMU and GPS data, and evaluate our positioning accuracy with respect to differential GPS.

关键词： 3D pose estimation image-based localization Micro aerial vehicle Outdoor Visual landmark

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Fusion of WiFi and Vision based on Smart Devices for Indoor localization 16

Fusion of WiFi and Vision based on Smart Devices for Indoor ...

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16th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry (VRCAI)

作者： Guo, Jing Zhang, Shaobo Zhao, Wanqing Peng, Jinye Northwest Univ Coll Informat Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781450360876

Indoor localization is an important problem with a wide range of applications such as indoor navigation, robot mapping, especially augmented reality(AR). One of most important tasks in AR technology is to estimate the target objects' position information in real environment. The existed AR systems mostly utilize specialized marker to locate, some AR systems track real 3D object in real environment but need to get the the position information of index points in environment in advance. The above methods are not efficiency and limit the application of AR system, so that solving indoor localization problem has significant meaning for the development of AR technology. The development of computer vision (CV) techniques and the ubiquity of intelligent devices with cameras provides the foundation for offering accurate localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct an densely sampled 3D model, which is a labor-intensive overhead for practical deployment. And a large amount of computation cost is difficult to satisfy the requirement for efficiency in mobile device. In this paper, we present iStart, a lightweight, easy deployed, image-based indoor localization system, which can be run on smart phone and VR/AR devices like HTC Vive, Google Glasses and so on. With core techniques rooted in data hierarchy scheme of WiFi fingerprints and photos, iStart also acquires user localization with a single photo of surroundings with high accuracy and short delay. Extensive experiments in various environments show that 90 percentile location deviations are less than 1 m, and 60 percentile location deviations are less than 0.5 m.

关键词： indoor localization image-based localization WiFi fingerprint smart devices CV model

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A Disaster Invariant Feature for localization

A Disaster Invariant Feature for Localization

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Soleimani, Behdad Ashtiani, Mohammad-Hassan Zokaei Soleimani, Behrouz Haji Moradi, Hadi Univ Tehran ECE Dept Control & Intelligent Proc Ctr Tehran 14174 Iran

ISBN: (纸本)9781424466757

In this paper we present a Disaster Invariant Feature (DIF), which is used for localization of Unmanned Aerial Vehicles (UAV). There exist numerous researches that address the problem of localization of UAVs using aerial images. However, after a disaster such as a tornado or an earthquake many features in aerial images like monuments and unique buildings may change, and the image-based localization would become hard or even impossible. Consequently it is important to find features that remain unchanged or with fairly small changes, and can be detected both before and after a disaster. We have used a recent method for street detection from aerial images and shown that road networks and segments are disaster invariant and could be utilized for localization and mapping. The algorithm has been implemented and tested on satellite images from Google, with nearly equivalent resolution to aerial images. The successful result of detecting this DIF on Port-au-Prince, in Haiti, images before and after the recent earthquake is presented.

关键词： DIF Google SLAM (robots) UAV localization aerial image resolution aircraft disaster invariant feature extraction disasters feature extraction geophysical image processing image resolution image-based localization mobile robots object detection remotely operated vehicles road networks satellite image street detection unmanned aerial vehicle

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A review of Visual-based localization 19

A review of Visual-Based Localization

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International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

作者： Xin, Xing Jiang, Jie Zou, Yin Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9781450372985

The visual-based localization (VBL) obtains the corresponding pose estimation in the localization system by utilizing various useful information in the surrounding environment, such as images, point cloud models, geometric information, semantic information. In recent years, visual-based localization (VBL) has been widely concerned by scientists, mainly because the commonly used GPS localization system cannot be effectively used in various environments. When GPS localization fails in some scenes such as very messy environments and severe signal occlusion, we can consider using visual-based localization to obtain the pose of the query images. Visual-based localization (VBL) has been widely used in the field of visual tasks, such as augmented reality, unmanned vehicle navigation, robotics, closed-loop detection, SFM (Structure from Motion) models. After years of development, the methods of visual-based localization (VBL) have been enriched and developed, In order to better understand the latest developments in VBL, overall research status and possible future development trends, we need make a systematic detailed classification of VBL. Although the predecessors have summarized the methods of VBL, due to the many new breakthroughs in VBL in recent years, the original summary is not perfect enough. So this paper will make a new and more detailed review of VBL in recent years. This paper divides the visualbased localization methods into three categories: image-based localization, localization based on learning model and localization based on 3D structure. And we also detail the principle, development of methods and the advantages and disadvantages of each method and future development trends.

关键词： Visual-based localization image-based localization localization based on learning model localization based on 3D structure

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