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Moreira D, Bharati A, Brogan J, Pinto A, Parowski M, Bowyer KW, Flynn PJ, Rocha A, Scheirer WJ. Image Provenance Analysis at Scale. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:6109-6123. [PMID: 30130187 DOI: 10.1109/tip.2018.2865674] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation (e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.
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152
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153
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Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired Pedestrians. SENSORS 2018; 18:s18082476. [PMID: 30065208 PMCID: PMC6111939 DOI: 10.3390/s18082476] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/25/2018] [Accepted: 07/26/2018] [Indexed: 11/17/2022]
Abstract
Localization systems play an important role in assisted navigation. Precise localization renders visually impaired people aware of ambient environments and prevents them from coming across potential hazards. The majority of visual localization algorithms, which are applied to autonomous vehicles, are not adaptable completely to the scenarios of assisted navigation. Those vehicle-based approaches are vulnerable to viewpoint, appearance and route changes (between database and query images) caused by wearable cameras of assistive devices. Facing these practical challenges, we propose Visual Localizer, which is composed of ConvNet descriptor and global optimization, to achieve robust visual localization for assisted navigation. The performance of five prevailing ConvNets are comprehensively compared, and GoogLeNet is found to feature the best performance on environmental invariance. By concatenating two compressed convolutional layers of GoogLeNet, we use only thousands of bytes to represent image efficiently. To further improve the robustness of image matching, we utilize the network flow model as a global optimization of image matching. The extensive experiments using images captured by visually impaired volunteers illustrate that the system performs well in the context of assisted navigation.
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154
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Wang X, Şekercioğlu YA, Drummond T, Frémont V, Natalizio E, Fantoni I. Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. SENSORS 2018; 18:s18082430. [PMID: 30049979 PMCID: PMC6111618 DOI: 10.3390/s18082430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 11/21/2022]
Abstract
In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250% and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.
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Affiliation(s)
- Xiaoqin Wang
- ARC Centre of Excellence for Robotic Vision, Monash University, Victoria 3800, Australia.
| | - Y Ahmet Şekercioğlu
- Université de Technologie de Compiègne, Sorbonne Universités, CNRS, UMR 7253 Heudiasyc-CS 60 319, 60203 Compiègne, France.
| | - Tom Drummond
- ARC Centre of Excellence for Robotic Vision, Monash University, Victoria 3800, Australia.
| | - Vincent Frémont
- Université de Technologie de Compiègne, Sorbonne Universités, CNRS, UMR 7253 Heudiasyc-CS 60 319, 60203 Compiègne, France.
| | - Enrico Natalizio
- Université de Technologie de Compiègne, Sorbonne Universités, CNRS, UMR 7253 Heudiasyc-CS 60 319, 60203 Compiègne, France.
| | - Isabelle Fantoni
- Ecole Centrale de Nantes, CNRS, UMR 6004 LS2N, 44300 Nantes, France.
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155
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Efficient 3D Objects Recognition Using Multifoveated Point Clouds. SENSORS 2018; 18:s18072302. [PMID: 30012990 PMCID: PMC6068497 DOI: 10.3390/s18072302] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 07/10/2018] [Accepted: 07/11/2018] [Indexed: 11/24/2022]
Abstract
Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods.
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156
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157
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Gawel A, Don CD, Siegwart R, Nieto J, Cadena C. X-View: Graph-Based Semantic Multi-View Localization. IEEE Robot Autom Lett 2018. [DOI: 10.1109/lra.2018.2801879] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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158
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Wu T, Liu J, Li Z, Liu K, Xu B. Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map. SENSORS 2018; 18:s18061974. [PMID: 29925779 PMCID: PMC6021798 DOI: 10.3390/s18061974] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 06/13/2018] [Accepted: 06/15/2018] [Indexed: 11/16/2022]
Abstract
Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three procedures. First, an indoor high-precision 3D photorealistic map is produced using a mobile mapping system, and the intrinsic and extrinsic parameters of the images are obtained from the mapping result. A point cloud is calculated using feature matching and multi-view forward intersection. Second, top-K similar images are queried using hamming embedding with SIFT feature description. Feature matching and pose voting are used to select correctly matched image, and the relationship between image points and 3D points is obtained. Finally, outlier points are removed using P3P with the coarse focal length. Perspective-four-point with unknown focal length and random sample consensus are used to calculate the intrinsic and extrinsic parameters of the query image and then to obtain the positioning of the smartphone. Compared with established baseline methods, the proposed method is more accurate and reliable. The experiment results show that 70 percent of the images achieve location error smaller than 0.9 m in a 10 m × 15.8 m room, and the prospect of improvement is discussed.
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Affiliation(s)
- Teng Wu
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Jingbin Liu
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
- Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China.
- Department of Remote Sensing and Photogrammetry and the Center of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute, 02430 Masala, Finland.
| | - Zheng Li
- Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China.
| | - Keke Liu
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Beini Xu
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
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159
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Frost D, Prisacariu V, Murray D. Recovering Stable Scale in Monocular SLAM Using Object-Supplemented Bundle Adjustment. IEEE T ROBOT 2018. [DOI: 10.1109/tro.2018.2820722] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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160
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161
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Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM. Auton Robots 2018. [DOI: 10.1007/s10514-018-9736-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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162
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Yu L, Jacobson A, Milford M. Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost. IEEE Robot Autom Lett 2018. [DOI: 10.1109/lra.2018.2792144] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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163
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Tang L, Wang Y, Ding X, Yin H, Xiong R, Huang S. Topological local-metric framework for mobile robots navigation: a long term perspective. Auton Robots 2018. [DOI: 10.1007/s10514-018-9724-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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164
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Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks. J INTELL ROBOT SYST 2018. [DOI: 10.1007/s10846-018-0804-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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165
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Bampis L, Amanatiadis A, Gasteratos A. Fast loop-closure detection using visual-word-vectors from image sequences. Int J Rob Res 2017. [DOI: 10.1177/0278364917740639] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this paper, a novel pipeline for loop-closure detection is proposed. We base our work on a bag of binary feature words and we produce a description vector capable of characterizing a physical scene as a whole. Instead of relying on single camera measurements, the robot’s trajectory is dynamically segmented into image sequences according to its content. The visual word occurrences from each sequence are then combined to create sequence-visual-word-vectors and provide additional information to the matching functionality. In this way, scenes with considerable visual differences are firstly discarded, while the respective image-to-image associations are provided subsequently. With the purpose of further enhancing the system’s performance, a novel temporal consistency filter (trained offline) is also introduced to advance matches that persist over time. Evaluation results prove that the presented method compares favorably with other state-of-the-art techniques, while our algorithm is tested on a tablet device, verifying the computational efficiency of the approach.
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Affiliation(s)
- Loukas Bampis
- Department of Production and Management Engineering, Democritus University of Thrace, Greece
| | - Angelos Amanatiadis
- Department of Production and Management Engineering, Democritus University of Thrace, Greece
| | - Antonios Gasteratos
- Department of Production and Management Engineering, Democritus University of Thrace, Greece
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166
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Hou Y, Zhang H, Zhou S. BoCNF: efficient image matching with Bag of ConvNet features for scalable and robust visual place recognition. Auton Robots 2017. [DOI: 10.1007/s10514-017-9684-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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167
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Evaluation of Object Proposals and ConvNet Features for Landmark-based Visual Place Recognition. J INTELL ROBOT SYST 2017. [DOI: 10.1007/s10846-017-0735-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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168
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169
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A Node-Based Method for SLAM Navigation in Self-Similar Underwater Environments: A Case Study. ROBOTICS 2017. [DOI: 10.3390/robotics6040029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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170
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Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination. SENSORS 2017; 17:s17112442. [PMID: 29068358 PMCID: PMC5713190 DOI: 10.3390/s17112442] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 11/17/2022]
Abstract
Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against the state-of-the-art SeqSLAM algorithm.
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171
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Meier K, Chung S, Hutchinson S. Visual‐inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points. J FIELD ROBOT 2017. [DOI: 10.1002/rob.21759] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Kevin Meier
- University of Illinois at Urbana‐Champaign Urbana Ilinois 61801
| | - Soon‐Jo Chung
- California Institute of Technology 1200 East California Boulevard, MC 105‐50 Pasadena California 91125
| | - Seth Hutchinson
- University of Illinois at Urbana‐Champaign Urbana Ilinois 61801
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172
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Perez-Grau FJ, Caballero F, Viguria A, Ollero A. Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation. INT J ADV ROBOT SYST 2017. [DOI: 10.1177/1729881417732757] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.
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Affiliation(s)
| | - Fernando Caballero
- Department of System Engineering and Automation, University of Seville, Seville, Spain
| | - Antidio Viguria
- Center for Advanced Aerospace Technologies (CATEC), La Rinconada, Sevilla, Spain
| | - Anibal Ollero
- Department of System Engineering and Automation, University of Seville, Seville, Spain
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173
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Affiliation(s)
- Shane Griffith
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
- GeorgiaTech Lorraine, Metz, France
| | - Georges Chahine
- GeorgiaTech Lorraine, Metz, France
- College of Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Cédric Pradalier
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
- GeorgiaTech Lorraine, Metz, France
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174
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Sujiwo A, Takeuchi E, Morales LY, Akai N, Darweesh H, Ninomiya Y, Edahiro M. Robust and Accurate Monocular Vision-Based Localization in Outdoor Environments of Real-World Robot Challenge. JOURNAL OF ROBOTICS AND MECHATRONICS 2017. [DOI: 10.20965/jrm.2017.p0685] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper describes our approach to perform robust monocular camera metric localization in the dynamic environments of Tsukuba Challenge 2016. We address two issues related to vision-based navigation. First, we improved the coverage by building a custom vocabulary out of the scene and improving upon place recognition routine which is key for global localization. Second, we established possibility of lifelong localization by using previous year’s map. Experimental results show that localization coverage was higher than 90% for six different data sets taken in different years, while localization average errors were under 0.2 m. Finally, the average of coverage for data sets tested with maps taken in different years was of 75%.
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175
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Affiliation(s)
- Özgür Erkent
- IIS, Computer Science, Innsbruck University, Innsbruck, Austria
| | - Hakan Karaog̃uz
- ICVAP, Computer Science and Communication, KTH, Stockholm, Sweden
| | - H. Işıl Bozma
- ISL, Electrical and Electronics Engineering, Boğaziçi University, İstanbul, Turkey
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176
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Arroyo R, Alcantarilla PF, Bergasa LM, Romera E. Are you ABLE to perform a life-long visual topological localization? Auton Robots 2017. [DOI: 10.1007/s10514-017-9664-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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177
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Wang K, Gou C, Zheng N, Rehg JM, Wang FY. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9569-z] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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178
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Mancini M, Bulo SR, Ricci E, Caputo B. Learning Deep NBNN Representations for Robust Place Categorization. IEEE Robot Autom Lett 2017. [DOI: 10.1109/lra.2017.2705282] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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179
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Bonardi F, Ainouz S, Boutteau R, Dupuis Y, Savatier X, Vasseur P. PHROG: A Multimodal Feature for Place Recognition. SENSORS 2017; 17:s17051167. [PMID: 28531101 PMCID: PMC5470912 DOI: 10.3390/s17051167] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/17/2017] [Accepted: 05/18/2017] [Indexed: 11/25/2022]
Abstract
Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges) with a Bag-of-Words approach. The tests we perform demonstrate that our method brings a significant improvement on the image retrieval issue in a visual place recognition context, particularly when there is a need to associate images from various spectral ranges such as infrared and visible: we have evaluated our approach using visible, Near InfraRed (NIR), Short Wavelength InfraRed (SWIR) and Long Wavelength InfraRed (LWIR).
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Affiliation(s)
- Fabien Bonardi
- Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes, Normandie University, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France.
| | - Samia Ainouz
- Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes, Normandie University, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France.
| | - Rémi Boutteau
- Institut de Recherche en Systèmes Électroniques Embarqués, Normandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.
| | - Yohan Dupuis
- Centre d'Études et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement, CEREMA, 76000 Rouen, France.
| | - Xavier Savatier
- Institut de Recherche en Systèmes Électroniques Embarqués, Normandie University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France.
| | - Pascal Vasseur
- Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes, Normandie University, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000 Rouen, France.
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180
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Liu D, Cong M, Zou Q, Du Y. A biological-inspired episodic cognitive map building framework for mobile robot navigation. INT J ADV ROBOT SYST 2017. [DOI: 10.1177/1729881417705922] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Dong Liu
- School of Mechanical Engineering, Dalian University of Technology, Dalian, China
| | - Ming Cong
- School of Mechanical Engineering, Dalian University of Technology, Dalian, China
| | - Qiang Zou
- School of Mechanical Engineering, Dalian University of Technology, Dalian, China
| | - Yu Du
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
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181
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182
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Schmidt T, Newcombe R, Fox D. Self-Supervised Visual Descriptor Learning for Dense Correspondence. IEEE Robot Autom Lett 2017. [DOI: 10.1109/lra.2016.2634089] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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183
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Han F, Yang X, Deng Y, Rentschler M, Yang D, Zhang H. SRAL: Shared Representative Appearance Learning for Long-Term Visual Place Recognition. IEEE Robot Autom Lett 2017. [DOI: 10.1109/lra.2017.2662061] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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184
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Hou Y, Zhang H, Zhou S. Tree-based indexing for real-time ConvNet landmark-based visual place recognition. INT J ADV ROBOT SYST 2017. [DOI: 10.1177/1729881416686951] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Recent impressive studies on using ConvNet landmarks for visual place recognition take an approach that involves three steps: (a) detection of landmarks, (b) description of the landmarks by ConvNet features using a convolutional neural network, and (c) matching of the landmarks in the current view with those in the database views. Such an approach has been shown to achieve the state-of-the-art accuracy even under significant viewpoint and environmental changes. However, the computational burden in step (c) significantly prevents this approach from being applied in practice, due to the complexity of linear search in high-dimensional space of the ConvNet features. In this article, we propose two simple and efficient search methods to tackle this issue. Both methods are built upon tree-based indexing. Given a set of ConvNet features of a query image, the first method directly searches the features’ approximate nearest neighbors in a tree structure that is constructed from ConvNet features of database images. The database images are voted on by features in the query image, according to a lookup table which maps each ConvNet feature to its corresponding database image. The database image with the highest vote is considered the solution. Our second method uses a coarse-to-fine procedure: the coarse step uses the first method to coarsely find the top- N database images, and the fine step performs a linear search in Hamming space of the hash codes of the ConvNet features to determine the best match. Experimental results demonstrate that our methods achieve real-time search performance on five data sets with different sizes and various conditions. Most notably, by achieving an average search time of 0.035 seconds/query, our second method improves the matching efficiency by the three orders of magnitude over a linear search baseline on a database with 20,688 images, with negligible loss in place recognition accuracy.
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Affiliation(s)
- Yi Hou
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, People’s Republic of China
| | - Hong Zhang
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Shilin Zhou
- College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, People’s Republic of China
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Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age. IEEE T ROBOT 2016. [DOI: 10.1109/tro.2016.2624754] [Citation(s) in RCA: 1565] [Impact Index Per Article: 195.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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