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VecMapLocNet: vision-based UAV localization using vector maps in GNSS-denied environments

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 225卷 362-381页

作者： Wang, Zhen Shi, Dianxi Qiu, Chunping Jin, Songchang Li, Tongyue Qiao, Ziteng Chen, Yang Acad Mil Sci Beijing 100850 Peoples R China Tianjin Artificial Intelligence Innovat Ctr Tianjin 300460 Peoples R China Peking Univ Beijing 100080 Peoples R China

vision-based localization techniques are effective UAV localization solutions for GNSS-denied conditions, however they depend on costly, complex, and seasonally variable satellite images or 3D maps, whereas humans can determine location using vector maps. Inspired by human navigation strategies, we propose VecMapLocNet , which uses vector maps to determine UAV 3-DoF poses (latitude, longitude, and yaw) through cross-modal matching. Three key modules are designed to improve the matching between UAV images and vector maps. The UAV feature extraction module is low-latency and adaptable to various flight altitudes, ensuring it is suitable for airborne deployment. The vector map feature extraction module employs a weighted representation of different map elements, ensuring robustness against changes in visual appearance. Inspired by Fourier transforms, the feature matching module for 3-DoF pose estimation is parameter-free, computationally efficient, and invariant to cross-modal differences. To evaluate VecMapLocNet, we introduce a comprehensive dataset that presents challenges, encompassing seven cities worldwide. Through rigorous experimentation, VecMapLocNet has demonstrated competitive performance compared to existing methods in localization accuracy (84.45% Recall@5 m), yaw estimation (88.61% Recall@5 degrees), and computational efficiency (25.23ms latency on onboard device Jetson Orin). Furthermore, we validated VecMapLocNet's performance in real-world scenarios, with experimental results confirming its generalization ability, achieving a localization error of 16.7 m and an orientation error of 3.1 degrees. The code and datasets are available at https://***.

关键词： UAV localization vision-based localization Vector maps GNSS-denied environments Cross-modal matching

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vision-based Target localization with Cooperative UAVs Towards Indoor Surveillance 98

Vision-Based Target Localization with Cooperative UAVs Towar...

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98th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Niu, Guanchong Cao, Qi Chen, Chung Shue Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China Paris Saclay Nokia Bell Labs F-91300 Massy France

ISBN: (纸本)9798350329285

Unmanned aerial vehicles (UAVs) have been widely adopted for a variety of civilian and military applications. Despite their many advantages, most UAVs are not suitable for indoor missions due to the lack of global position system (GPS) and the large size of UAV with diverse sensors. In this work, a lightweight and cost-effective indoor multi-UAV surveillance system is presented for accurate target localization. The proposed system employs a vision-based architecture, leveraging ORB-SLAM for self-localization and YOLOv3 for object detection. The triangulation method is employed for target positioning, offering reliable performance with negligible time delays and improved detection compared to depth camera approaches. After that, the Riccati observer is introduced to address the stochastic nature of the system. Through a series of experiments involving real UAVs, the system demonstrates its ability to accurately localize and track targets in indoor environments, updating the UAVs' positions in real-time with optimal performance.

关键词： Cooperative UAV Systems Indoor Surveillance vision-based localization Riccati Observer

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Control in belief space with temporal logic specifications using vision-based localization

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2019年第6期38卷 702-722页

作者： Leahy, Kevin Cristofalo, Eric Vasile, Cristian-Ioan Jones, Austin Montijano, Eduardo Schwager, Mac Belta, Calin MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA Stanford Univ Stanford CA 94305 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Zaragoza Zaragoza Spain Boston Univ Boston MA 02215 USA

We present a solution for operating a vehicle without global positioning infrastructure while satisfying constraints on its temporal behavior, and on the uncertainty of its position estimate. The proposed solution is an end-to-end framework for mapping an unknown environment using aerial vehicles, synthesizing a control policy for a ground vehicle in that environment, and using a quadrotor to localize the ground vehicle within the map while it executes its control policy. This vision-based localization is noisy, necessitating planning in the belief space of the ground robot. The ground robot's mission is given using a language called Gaussian Distribution Temporal Logic (GDTL), an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. We use a sampling-based algorithm to generate a transition system in the belief space and use local feedback controllers to break the curse of history associated with belief space planning. To localize the vehicle, we build a high-resolution map of the environment by flying a team of aerial vehicles in formation with sensor information provided by their onboard cameras. The control policy for the ground robot is synthesized under temporal and uncertainty constraints given the semantically labeled map. Then the ground robot can execute the control policy given pose estimates from a dedicated aerial robot that tracks and localizes the ground robot. The proposed method is validated using two quadrotors to build a map, followed by a two-wheeled ground robot and a quadrotor with a camera for ten successful experimental trials.

关键词： Persistent specifications distribution temporal logic belief space planning homography-based formation control vision-based localization sampling-based planning

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Quadrotor vision-based localization for Amphibious Robots in Amphibious Area 16

Quadrotor Vision-based Localization for Amphibious Robots in...

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16th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Xing, Huiming Guo, Shuxiang Shi, Liwei Hou, Xihuan Liu, Yu Hu, Yao Xia, Debin Li, Zan Beijing Inst Technol Key Lab Convergence Med Engn Syst & Healthcare Te Minist Ind & Informat Technol Sch Life Sci 5 Zhongguancun South St Beijing 100081 Peoples R China Beijing Inst Technol Minist Educ Key Lab Biomimet Robots & Syst 5 Zhongguancun South St Beijing 100081 Peoples R China Kagawa Univ Fac Engn 2217-20 Hayashi Cho Takamatsu Kagawa Japan

ISBN: (纸本)9781728116976

Considering imaging qualities and air-water medium changes, the localization of multiple amphibious robots in GPS-denied outdoors is a great challenge. This paper presents a vision-based localization approach for multiple amphibious robots in amphibious environment using a quadrotor hovering over the head of robots. In terms of the circular shape observed by the quadrotor on land, a shape and color-based detection method is designed to identify robots. An improved Hough transform was used to speed up the shape detection of our robot. Then we use the color information to identify different robots. In water, ASRobot is able to realize multiple motion with different configurations of legs. Therefore, in view of different shapes generated by different configurations, a multiple size-varying template matching method is utilized to recognize different robots in water. On account of the refraction of rays, the vision-based localization model was built in amphibious environment. Finally, experiments of localization were conducted, and the results verified the feasibility of the proposed vision-based localization approach for ASRobots.

关键词： Amphibious Spherical Robots vision-based localization quadrotor-based localization

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TAKING ME TO THE CORRECT PLACE: vision-based localization FOR AUTONOMOUS VEHICLES 26

TAKING ME TO THE CORRECT PLACE: VISION-BASED LOCALIZATION FO...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Lu, Guoyu Wong, Xue-Iuan Rochester Inst Technol Rochester NY 14623 USA Ford Motor Co Dearborn MI 48121 USA

ISBN: (纸本)9781538662496

Vehicle localization is a critical component for autonomous driving, which estimates the position and orientation of vehicles. To achieve the goal of quick and accurate localization, we develop a system that can dynamically switch the features applied for localization. Specifically, we develop a feature based on convolutional neural network targeting at accurate matching, which proves high rotation invariant property that can help to overcome the relatively large error when vehicles turning at corners. However, when the vehicle motion mainly involves translation, we apply the ORB feature to localize the vehicle, as it demonstrates similar accuracy in translation estimation. Through dynamic switching between features, we can accurately localize the vehicle with high time performance. To filter out noise features, we train a CNN neural network to semantically understand the images and filter the features from moving objects and infinity position. During the pose estimation stage, we rely on the depth of the 3D points to identify the inliers satisfying RANSAC. Experiments demonstrate the superb performance of our method.

关键词： vision-based localization Convolutional neural network Pose estimation

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vision-based Global localization of Unmanned Aerial Vehicles with Street View Images 37

Vision-based Global Localization of Unmanned Aerial Vehicles...

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37th Chinese Control Conference (CCC)

作者： Yan, Xuejiao Shi, Zongying Zhong, Yisheng Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563958

In this paper, a vision-based method is proposed to globally localize an unmanned aerial vehicle (UAV) flying in the urban scenarios using Google Street View as prior information. Since the trajectory of the UAV with absolute scale cannot be recovered with the help of monocular visual odometry only, we leverage the known positional information of Google Street View to localize vehicles with respect to the surrounding buildings. First, a place recognition algorithm is presented to solve air-ground matching problem by searching aerial images taken from onboard camera within the geotagged image database, Then based on the matched images by place recognition and tracked features via visual odometry, a novel absolute scale estimation mechanism is proposed to estimate the pose of UAV in the real world coordinate system and to correct the scale drift of visual odometry. Furthermore, the pose graph with UAV's poses as vertexes and relative transformations as edges is optimized with SE(3) constraints to get trajectory of UAV in the real world coordinate system. Experiments are conducted on a 2km dataset recorded onboard by a caniera-ectuipped UAV flying in the urban streets of Zurich, Switzerland, and the results show that our method is able to metrically localize the UAV without previously visiting or building 3D models in advance.

关键词： vision-based localization unmanned aerial vehicle Google Street View absolute scale estimation

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vision-based Global localization of Unmanned Aerial Vehicles with Street View Images

Vision-based Global Localization of Unmanned Aerial Vehicles...

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第37届中国控制会议

作者： Xuejiao Yan Zongying Shi Yisheng Zhong Department of Automation Tsinghua University

In this paper, a vision-based method is proposed to globally localize an unmanned aerial vehicle(UAV) flying in the urban scenarios using Google Street View as prior information. Since the trajectory of the UAV with absolute scale cannot be recovered with the help of monocular visual odometry only, we leverage the known positional information of Google Street View to localize vehicles with respect to the surrounding buildings. First, a place recognition algorithm is presented to solve air-ground matching problem by searching aerial images taken from onboard camera within the geotagged image database. Then based on the matched images by place recognition and tracked features via visual odometry, a novel absolute scale estimation mechanism is proposed to estimate the pose of UAV in the real world coordinate system and to correct the scale drift of visual odometry. Furthermore, the pose graph with UAV's poses as vertexes and relative transformations as edges is optimized with SE(3) constraints to get trajectory of UAV in the real world coordinate system. Experiments are conducted on a 2km dataset recorded onboard by a camera-equipped UAV flying in the urban streets of Zurich, Switzerland, and the results show that our method is able to metrically localize the UAV without previously visiting or building 3D models in advance.

关键词： vision-based localization unmanned aerial vehicle Google Street View absolute scale estimation

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Evaluation of Local Descriptors for vision-based localization of Humanoid Robots 7

Evaluation of Local Descriptors for Vision-Based Localizatio...

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7th Mexican Conference on Pattern Recognition (MCPR)

作者： Aldana-Murillo, Noe G. Hayet, Jean-Bernard Becerra, Hector M. Ctr Invest Matemat CIMAT Guanajuato 36240 Gto Mexico

ISBN: (纸本)9783319192642;9783319192635

In this paper, we address the problem of appearance-based localization of humanoid robots in the context of robot navigation using a visual memory. This problem consists in determining the most similar image belonging to a previously acquired set of key images (visual memory) to the current view of the monocular camera carried by the robot. The robot is initially kidnapped and the current image has to be compared with the visual memory. We tackle the problem by using a hierarchical visual bag of words approach. The main contribution of the paper is a comparative evaluation of local descriptors to represent the images. Real-valued, binary and color descriptors are compared using real datasets captured by a small-size humanoid robot. A specific visual vocabulary is proposed to deal with issues generated by the humanoid locomotion: blurring and rotation around the optical axis.

关键词： vision-based localization Humanoid robots Local descriptors comparison Visual bag of words

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vision-based GNSS-Free localization for UAVs in the Wild 7

Vision-Based GNSS-Free Localization for UAVs in the Wild

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7th International Conference on Mechanical Engineering and Robotics Research (ICMERR)

作者： Gurgu, Marius-Mihail Queralta, Jorge Pena Westerlund, Tomi Univ Turku Turku Intelligent Embedded & Robot Syst TIERS Lab Turku Finland

ISBN: (纸本)9781665490511

Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth. Compared to state-of-the-art Visual Odometry (VO) approaches, our solution is designed for long-distance, high-altitude UAV flights. Code and datasets are available at https://***/TIERS/wildnav.

关键词： UAV MAV GNSS-Free GNSS-denied vision-based localization Photogrammetry Computer vision Perception-based localization Visual Odometry

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A vision-based System for Robot localization in Large Industrial Environments

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2020年第2期99卷 359-370页

作者： Boutteau, Remi Rossi, Romain Qin, Lei Merriaux, Pierre Savatier, Xavier Normandie Univ UNIROUEN ESIGELEC IRSEEM F-76000 Rouen France

In this paper, we propose a vision-based system to localize mobile robots in large industrial environments. Our contributions rely on the use of fisheye cameras to have a large field of view and the associated algorithms. We propose several calibration methods and evaluate them with a ground-truth obtained by a motion capture system. In these experiments, we also evaluate the influence of the parameters as the number of points used for calibration, or the influence of the accuracy of these points. Our system is then experimented in a real industrial environment, where we localize an ROV (Remotely Operated underwater Vehicle) in a basin. As shown is this paper, this system can also localize wheeled mobile robots in the same way.

关键词： vision-based localization Fisheye cameras Robust pose estimation Extrinsic calibration

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