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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第3期34卷 1630-1644页

作者： Koku, Ahmet Bugra Orta Dogu Tekn Univ Muhendisl Fak Makina Muhendisligi Bolumu TR-06800 Ankara Turkey

Advances in robotics liberated robots from factory floors by the end of 20th century. Use of robots in our daily lives is only expected to increase in time. Robots, while relieving us of the burden of tedious, hard and dangerous tasks, most of them are still expected to be territorial, i.e. they will operate in a predefined or rather bounded environment. For enhanced performance, a robot should be familiar with its territory. In this work, use of skylines extracted from panoramic images is studied in order to provide localization. To achieve localization, first a special map is prepared using a series of panoramic images taken from the territory. images, those taken from the vicinity of this map at different times and under different weather conditions, are localized on this map. Given that the proposed method only focuses on a small portion of the panoramic image, it is relatively easier to implement in various hardware platforms (especially those with limited computational capabilities) in comparison to the methods that process the whole image.

关键词： Panoramic images skyline robot localization image-based localization territorial robots

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View Synthesis with Scene Recognition for Cross-View image localization

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FUTURE INTERNET 2023年第4期15卷 126-126页

作者： Lee, Uddom Jiang, Peng Wu, Hongyi Xin, Chunsheng Old Dominion Univ Sch Cybersecur Norfolk VA 23529 USA Old Dominion Univ Dept ECE Norfolk VA 23529 USA Univ Arizona Dept ECE Tucson AZ 85721 USA

image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions.

关键词： image-based localization deep learning generative neural networks style transfer

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ventura, Jonathan Kukelova, Zuzana Sattler, Torsten Barath, Daniel Cal Poly Dept Comp Sci & Software Engn San Luis Obispo CA USA Czech Tech Univ Visual Recognit Grp Fac Elect Engn Prague Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Swiss Fed Inst Technol Dept Comp Sci Comp Vision & Geometry Grp Zurich Switzerland

ISBN: (纸本)9798350353006

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https: //***/danini/absolute-pose-from-orientedand-scaled-features.

关键词： absolute pose computer vision geometric vision image-based localization minimal solver

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Preserving Privacy in Mobile Spatial Computing 32

Preserving Privacy in Mobile Spatial Computing

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32nd Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) Part of MMSys Conference

作者： Wu, Nan Cheng, Ruizhi Chen, Songqing Han, Bo George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781450393836

Mapping and localization are the key components in mobile spatial computing to facilitate interactions between users and the digital model of the physical world. To enable localization, mobile devices keep capturing images of the real-world surroundings and uploading them to a server with spatial maps for localization. This leads to privacy concerns on the potential leakage of sensitive information in both spatial maps and localization images (e.g., when used in confidential industrial settings or our homes). Motivated by the above issues, we present a holistic research agenda in this paper for designing principled approaches to preserve privacy in spatial mapping and localization. We introduce our ongoing research, including learning-assisted noise generation to shield spatial maps, distributed architecture with intelligent aggregation to protect localization images, and end-to-end privacy preservation with fully homomorphic encryption. We also discuss the technical challenges, our preliminary results, and open research problems in those areas.

关键词： Mobile Spatial Computing 3D Spatial Maps image-based localization Privacy Leakage Structure from Motion Fully Homomorphic Encryption

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Technologies for Under-Ice AUV Navigation

Technologies for Under-Ice AUV Navigation

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IEEE OES Joint Symposium/Workshop on Autonomous Underwater Vehicles (AUV)

作者： Bandara, Doupadi Leong, Zhi Nguyen, Hung Jayasinghe, Shantha Forrest, Alexander L. Univ Tasmania Australian Maritime Collage Hobart Tas 7001 Australia Univ Calif Davis Civil & Environm Engn Davis CA 95616 USA

ISBN: (纸本)9781509024421

Approximately 12% of the world's oceans are covered by ice. Understanding the physical processes, ecosystem structure, mixing dynamics and the role of these inaccessible environments in the context of global climate change is extremely important. Autonomous Underwater Vehicles (AUVs) play a major role in the potential exploration of these water systems due to the challenges of human access and relatively high associated risk. That said, AUV navigation and localization is challenging in these environments due to the unavoidable growth of navigational drift associated with inertial navigation systems, especially in long range missions under ice where surfacing in open water is not possible. While acoustic transponders have been used, they are time consuming and difficult to deploy. Terrain Relative Navigation (TRN) and Simultaneous localization and Mapping (SLAM) based technologies are emerging in recent years as promising navigation solutions as they neither require deploying navigational aids or calculating the distance travelled from a reference point to determine location. One of the key challenges of underwater or under-ice image based localization results from the unstructured nature and lack of significant features in underwater environments. This issue has motivated the review presented in this paper, which outlines a potential area of under ice AUV navigation and localization by combining TRN and SLAM with image matching methods for navigation in featureless environments.

关键词： Autonomous underwater vehicle (AUV) image-based localization featureless environment Simultaneous localization and Mapping (SLAM) Under-ice navigation

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Learning Spatial-Aware Cross-View Embeddings for Ground-to-Aerial Geolocalization 10th

Learning Spatial-Aware Cross-View Embeddings for Ground-to-A...

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10th International Conference on image and Graphics (ICIG)

作者： Cao, Rui Zhu, Jiasong Li, Qing Zhang, Qian Li, Qingquan Liu, Bozhi Qiu, Guoping Univ Nottingham Int Doctoral Innovat Ctr Ningbo Peoples R China Univ Nottingham Sch Comp Sci Ningbo Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9783030341206;9783030341190

image-based geolocalization is an important alternative to GPS-based localization in GPS-denied situations. Among them, ground-to-aerial geolocalization is particularly promising but also difficult due to drastic viewpoint and appearance differences between ground and aerial images. In this paper, we propose a novel spatial-aware Siamese-like network to address the issue by exploiting the spatial transformer layer to effectively alleviate the large view variation and learn location discriminative embeddings from the cross-view images. Furthermore, we propose to combine the triplet ranking loss with a simple and effective location identity loss to further enhance the performances. We test our method on a publicly available dataset and the results show that the proposed method outperforms state-of-the-art by a large margin.

关键词： image-based localization Cross-view geolocalization image retrieval Deep metric learning

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OPTIMAL SELECTION OF SUBSET OF imageS WITH HIGHEST INTRA-CLASS SIMILARITY FOR 3D SCENE RECONSTRUCTION

OPTIMAL SELECTION OF SUBSET OF IMAGES WITH HIGHEST INTRA-CLA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Salarian, Mahdi Ansari, Rashid Univ Illinois Elect & Comp Engn Dept Chicago IL 60607 USA

ISBN: (纸本)9781538646588

Finding the accurate location of a mobile device based on images it acquires usually requires applying structure from motion (SFM) for 3D camera position reconstruction. Since the convergence of SFM depends on effectively selecting among the multiple retrieved images, we propose an optimization framework to do make the selection using the criterion of the highest intra-class similarity among images returned from retrieval pipeline. The selection process should consider only images with distinct GPS-tags. The selected images along with the query can be used to reconstruct a 3D scene and obtain relative camera positions. Experimental results demonstrate our method achieves a higher convergence rate in the SFM processing.

关键词： image-based localization BOF Retrieval

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BoQ: A Place is Worth a Bag of Learnable Queries

BoQ: A Place is Worth a Bag of Learnable Queries

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ali-Bey, Amar Chaib-Draa, Brahim Giguere, Philippe Univ Laval Dept Comp Sci & Software Engn Quebec City PQ Canada

ISBN: (纸本)9798350353006

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries, designed to capture universal place-specific attributes. Unlike existing techniques that employ self-attention and generate the queries directly from the input, BoQ employ distinct learnable global queries, which probe the input features via cross-attention, ensuring consistent information aggregation. In addition, this technique provides an interpretable attention mechanism and integrates with both CNN and Vision Transformer backbones. The performance of BoQ is demonstrated through extensive experiments on 14 large-scale benchmarks. It consistently outperforms current state-of-the-art techniques including NetVLAD, MixVPR and EigenPlaces. Moreover, despite being a global retrieval technique (one-stage), BoQ surpasses two-stage retrieval methods, such as Patch-NetVLAD, TransVPR and R2Former, all while being orders of magnitude faster and more efficient. The code and model weights are publicly available at https://***/amaralibey/Bag-of-Queries.

关键词： image Geo-localization image Retrieval image-based localization NetVLAD Place Recognition Visual localization Visual Place Recognition VPR

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Multi-descriptor retrieval in digitalized photographs collections

Multi-descriptor retrieval in digitalized photographs collec...

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作者： Neelanjan Bhowmik Universite Paris-Est

学位级别：博士

Content-based image Retrieval (CBIR) is a discipline of Computer Science which aims at auto- matically structuring image collections according to some visual criteria. The offered function- alities include the efficient access to images in a large database of images, or the identification of their content through object detection and recognition tools. They impact a large range of fields which manipulate this kind of data, such as multimedia, culture, security, health, scientific research, etc. To index an image from its visual content first requires producing a visual summary of this content for a given use, which will be the index of this image in the database. From now on, the literature on image descriptors is very rich; several families of descriptors exist and in each family, a lot of approaches live together. Many descriptors do not describe the same information and do not have the same properties. Therefore it is relevant to combine some of them to better describe the image content. The combination can be implemented differently according to the involved descriptors and to the application. In this thesis, we focus on the family of local descriptors, with application to image and object retrieval by example in a collection of images. Their nice properties make them very popular for retrieval, recognition and categorization of objects and scenes. Two directions of research are investigated: Feature combination applied to query-by-example image retrieval: The core of the thesis rests on the proposal of a model for combining low-level and generic descriptors in order to obtain a descriptor richer and adapted to a given use case while maintaining genericity in order to be able to index different types of visual contents. The considered application being query-by- example, another major difficulty is the complexity of the proposal, which has to meet with reduced retrieval times, even with large datasets. To meet these goals, we propose an approach based on the fus

关键词： Content-based image retrieval (CBIR) Feature extraction Interest points Feature combination Bag-of-Features (BoF) Inverted index Spatial complementarity Cultural heritage photography Data linking image exploration Cross-domain image retrieval image-based localization

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Lightweight Mobile Devices Indoor Location based on image Database 26

Lightweight Mobile Devices Indoor Location Based on Image Da...

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26th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Gao, Ran Zhao, Yanchao Tang, Maoxing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China

ISBN: (纸本)9781728190747

Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloudbased scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate ondevice image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).

关键词： image-based localization real-time smartphone-based localization indoor location image database update

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