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A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

REMOTE SENSING 2024年第22期16卷 4220-4220页

作者： Wang, Zimu Li, Xiunan Chen, Peng Luo, Dan Zheng, Gang Chen, Xin Minist Nat Resources Inst Oceanog 2 Hangzhou 310012 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200230 Peoples R China

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

关键词： resistance to shaking visual simultaneous localization and mapping ship target ranging

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Stereo vision SLAM-based 3D reconstruction on UAV development platforms

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JOURNAL OF ELECTRONIC IMAGING 2023年第1期32卷 013041-013041页

作者： Ding, Chenglong Dai, Yunfeng Feng, Xingming Zhou, Yaqin Li, Qingwu Hohai Univ Coll Internet Things Engn Changzhou Peoples R China State Grid Yancheng Power Supply Co Yancheng Peoples R China Key Lab Sensor Networks & Environm Sensing Changzhou Peoples R China

Three-dimensional (3D) reconstruction of unmanned aerial vehicle (UAV) images has garnered a lot of attention with the rapid growth of UAV technology. The crucial technique for large-scale 3D reconstruction of images taken from UAV is to get accurate and drift-free state estimation of UAV. Vision-based simultaneous localization and mapping (SLAM) system is widely used in this field because of the function of robot localization and unknown environment mapping. However, the current visual SLAM systems are vulnerable to issues including feature extraction challenge, mismatching, cumulative drift, and tracking lost in complicated and low-texture environment. To cope with the aforementioned challenges, we build a stereo vision SLAM framework based on ORB-SLAM, which fuses global positioning system (GPS) information and dense matching results based on deep learning to achieve accurate and drift-free pose estimation and 3D reconstruction of UAV images. We utilize cutting-edge method to create pixel-wise dense matching of adjacent frames and stereo frames and make full use of them. Then, to minimize the accumulated drifts, global GPS data are combined with pose graph optimization, which aligns local estimation with the global coordinate. Finally, the globally accurate and drift-free pose estimations are used to build a sparse point cloud map. Patch-based multi-view stereo algorithm is implemented to construct a dense map based on UAV images for reasonable results. We evaluate the effectiveness and accuracy of our approach in both simulation and real-world experiments. Extensive experimental results demonstrate that our method achieves great results.

关键词： unmanned aerial vehicle three-dimensional reconstruction visual simultaneous localization and mapping dense matching global positioning system

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Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

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OPTICAL ENGINEERING 2023年第4期62卷 048102-048102页

作者： Dong, Yunlong Guo, Wei Li, Mantian Zha, Fusheng Shao, Bing Sun, Lining Harbin Inst Technol Sch Mechatron Engn State Key Lab Robot & Syst Harbin Peoples R China Shenzhen Acad Aerosp Technol Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China

Image degradation caused by bad weather, such as haze, rain, and snow, significantly degrades a vision system's performance, imposing challenges to the autonomous motion of mobile robots. Therefore, it is crucial to restore the degraded images for mobile robots operating outdoors. Spurred by this concern, a framework that restores degraded images and affords simultaneous localization and mapping (SLAM) under multiple bad weather conditions is presented. Specifically, the developed architecture combines the advantages of convolutional neural network features and weather features. It improves the accuracy of identifying the degradation type by developing a weather inference module based on ensemble learning. According to the internal mechanism of image degradation, a degraded image restoration method is proposed utilizing physical models, with a subsequent operation refining the preliminary restored results. To improve our method's adaptability to real scenes, unpaired real-world weather images are introduced into the degradation removal algorithm through generative adversarial networks. The proposed weather persistent assumption combines weather inference and degradation restoration modules in the SLAM system to capture multiple weather conditions, which improves the system's accuracy and running speed. Comprehensive experiments evaluating the developed framework and its components highlight that the proposed weather inference and degraded image restoration methods achieve a highly appealing effect. The final experimental results demonstrate that our framework autonomously identifies weather types, triggers the corresponding restoration, and realizes accurate localization in multiple bad weather conditions. It affords SLAM accuracy under multiple bad weather conditions that is close to raw SLAM in clear weather.

关键词： multiple bad weather conditions weather inference degraded image restoration visual simultaneous localization and mapping

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Loop Closure Detection Method Based on Similarity Differences between Image Blocks

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SENSORS 2023年第20期23卷 8632-8632页

作者： Huang, Yizhe Huang, Bin Zhang, Zhifu Shi, Yuanyuan Yuan, Yizhao Sun, Jinfeng Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Dongfeng Liuzhou Motor Co Ltd Liuzhou 545005 Peoples R China Hainan Univ Sch Mech & Elect Engn Haikou 570228 Peoples R China

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy;said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%;this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.

关键词： visual simultaneous localization and mapping loop closure detection similarity difference convolutional neural network

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SVG-Loop: Semantic-visual-Geometric Information-Based Loop Closure Detection

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REMOTE SENSING 2021年第17期13卷 3520-3520页

作者： Yuan, Zhian Xu, Ke Zhou, Xiaoyu Deng, Bin Ma, Yanxin Natl Univ Def Technol Coll Elect Sci Changsha 410073 Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410073 Peoples R China Hunan Key Lab Marine Detect Technol Changsha 410073 Peoples R China

Loop closure detection is an important component of visual simultaneous localization and mapping (SLAM). However, most existing loop closure detection methods are vulnerable to complex environments and use limited information from images. As higher-level image information and multi-information fusion can improve the robustness of place recognition, a semantic-visual-geometric information-based loop closure detection algorithm (SVG-Loop) is proposed in this paper. In detail, to reduce the interference of dynamic features, a semantic bag-of-words model was firstly constructed by connecting visual features with semantic labels. Secondly, in order to improve detection robustness in different scenes, a semantic landmark vector model was designed by encoding the geometric relationship of the semantic graph. Finally, semantic, visual, and geometric information was integrated by fuse calculation of the two modules. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference.

关键词： loop closure detection bag of words panoptic segmentation visual simultaneous localization and mapping

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ImPL-VIO: An Improved Monocular visual-Inertial Odometry Using Point and Line Features 13th

ImPL-VIO: An Improved Monocular Visual-Inertial Odometry Usi...

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13th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Cheng, Haoqi Wang, Hong Gan, Zhongxue Deng, Jinxiang Harbin Inst Technol Sch Mech Engn & Automat Shenzhen Peoples R China Engn Res Ctr Intelligent Robot Ji Hua Lab Foshan Guangdong Peoples R China

ISBN: (纸本)9783030666453;9783030666446

Most of the visual-inertial navigation systems (VINS) that use only point features usually work well in regular environment, but decay in low-texture scenes. Meanwhile, those systems rarely construct environmental map with structural information. In this paper, an improved tightly-coupled monocular visual-inertial odometry (ImPL-VIO) is developed. The whole system is composed of point and line feature tracking, inertial measurements processing, pose estimator and loop closure detection. For the better use of monocular line observations in the sliding window based pose estimator, an improved line triangulation algorithm is proposed after a detailed analysis of error sources. In addition, we, for the first time, employ the closest point (CP) representation for spatial lines to optimization-based VINS system, and derive the corresponding Jacobians analytically. Finally, simulation and real-world experiments are conducted to validate the proposed system.

关键词： visual-inertial odometry visual simultaneous localization and mapping Line segment features

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Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第3期17卷

作者： Feng, Youyang Wang, Qing Yang, Gaochao Southeast Univ Instrumental Sci & Engn Nanjing 210000 Jiangsu Peoples R China

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universitat Munchen (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures;Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

关键词： Pose graph optimization computer vision visual simultaneous localization and mapping optimization method

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Improving feature-based visual SLAM in Person Indoor Navigation with HDR Imaging 2

Improving feature-based visual SLAM in Person Indoor Navigat...

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2nd IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Albrecht, Alexander Heide, Nina Felicitas Fraunhofer Inst Optron Syst Technol & Image Explo Syst Measurement Control & Diag Karlsruhe Germany

ISBN: (纸本)9781728151021

Drastically changing illumination conditions and high dynamic ranges lead to over- and underexposed image areas, which challenge feature detection. As a result the quality of feature-based visual simultaneous localization and mapping (SLAM) suffers. This work addresses the problem by using High Dynamic Ranging (HDR) image data as visual input. Utilizing HDR cameras with two prism-mounted CCDs allows fusion of two synchronized video streams. This paper analyzes various HDR fusion methods focusing on the suitability for feature-based visual SLAM. The fusion methods are evaluated on a person carried HDR stereo camera system under realistic indoor application scenarios. Changes of illumination are provoked by cameras facing windows with bright luminance. It has been demonstrated, that our method is capable of handling changing illumination conditions, which results in a more stable and reliable visual SLAM solution.

关键词： Indoor localization visual simultaneous localization and mapping High Dynamic Ranging Prism-Mounted CCDs

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Review of vision-based simultaneous localization and mapping 3

Review of vision-based Simultaneous Localization and Mapping

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Li, Ang Ruan, Xiaogang Huang, Jing Zhu, Xiaoqing Wang, Fei Beijing Univ Technol Coll Elect & Control Engn Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing Peoples R China

ISBN: (纸本)9781538662434

Vision-based simultaneous localization and mapping (VSLAM) which uses visual sensor to make a robot locate itself in an unknown environment while simultaneously construct a map of the environment. With the continuous development of computer vision and robotics, VSLAM has become a supporting technology for popular fields such as unmanned aerial vehicle, virtual reality and unmanned driving. In this paper, the classical framework of visual SLAM is introduced briefly. On this basis, the key technologies and latest research progress of VSLAM from indirect and direct methods are surveyed. Then the research progress of deep learning techniques applied to VSLAM is reviewed. Finally, the development tendency of VSLAM is discussed.

关键词： visual simultaneous localization and mapping robot visual odometry graph optimization loop closure detection

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B-SLAM-SIM: A Novel Approach to Evaluate the Fusion of visual SLAM and GPS by Example of Direct Sparse Odometry and Blender 14

B-SLAM-SIM: A Novel Approach to Evaluate the Fusion of Visua...

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

作者： Kalisz, Adam Particke, Florian Penk, Dominik Hiller, Markus Thielecke, Joern Friedrich Alexander Univ Erlangen Nurnberg Dept Elect Elect & Commun Engn Informat Technol Wolfsmantel 33 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Comp Graph Lab LGDV Cauerstr 11 Erlangen Germany

ISBN: (纸本)9789897583544

In order to account for sensor deficiancies, usually a multi-sensor approach is used where various sensors complement each other. However, synchronization of highly accurate Global Positioning System (GPS) and video measurements requires specialized hardware which is not straightforward to set up. This paper proposes a full simulation environment for data generation and evaluation of visual simultaneous localization and mapping (visual SLAM) and GPS based on free and open software. Specifically, image data is created by rendering a virtual environment where camera effects such as Motion Blur and Rolling Shutter can be added. Consequently, a ground truth camera trajectory is available and can be distorted via additive Gaussian noise to understand all parameters involved in the use of fusion algorithms such as the Kalman Filter.

关键词： Fusion Global Positioning System visual simultaneous localization and mapping GPS SLAM Simulation Blender

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