Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.展开更多
Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,...Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.展开更多
地热作为绿色低碳的可再生能源,在能源领域具有重要意义。为了探究当前国内外学者在地热领域的研究方向和热点,明确地热研究的发展前景,对2000—2023年中国知网(CNKI)和Web of Science(WoS)数据库所收录的地热领域高质量文献进行检索,...地热作为绿色低碳的可再生能源,在能源领域具有重要意义。为了探究当前国内外学者在地热领域的研究方向和热点,明确地热研究的发展前景,对2000—2023年中国知网(CNKI)和Web of Science(WoS)数据库所收录的地热领域高质量文献进行检索,并利用VOS viewer进行可视化分析,掌握数据库年发文量、国家和机构、期刊发文量、高被引文献及文献所涉及的关键词。研究结果表明,在2000—2023年间,地热研究方向中英文文献的发文量总体均呈上升趋势;在发文机构方面,中国地质大学、中国科学院等中国科研机构已跃升为发文的重要基石,主要发表在《水文地质工程地质》《Geothermics》《太阳能学报》《Geothermics》的文献最多,为1213篇;地热领域研究主要集中于地热资源勘探与开发。研究可为科研人员全面了解2000年至今的地热研究现状与未来发展趋势提供参考。展开更多
Error or drift is frequently produced in pose estimation based on geometric"feature detection and tracking"monocular visual odometry(VO)when the speed of camera movement exceeds 1.5 m/s.While,in most VO meth...Error or drift is frequently produced in pose estimation based on geometric"feature detection and tracking"monocular visual odometry(VO)when the speed of camera movement exceeds 1.5 m/s.While,in most VO methods based on deep learning,weight factors are in the form of fixed values,which are easy to lead to overfitting.A new measurement system,for monocular visual odometry,named Deep Learning Visual Odometry(DLVO),is proposed based on neural network.In this system,Convolutional Neural Network(CNN)is used to extract feature and perform feature matching.Moreover,Recurrent Neural Network(RNN)is used for sequence modeling to estimate camera’s 6-dof poses.Instead of fixed weight values of CNN,Bayesian distribution of weight factors are introduced in order to effectively solve the problem of network overfitting.The 18,726 frame images in KITTI dataset are used for training network.This system can increase the generalization ability of network model in prediction process.Compared with original Recurrent Convolutional Neural Network(RCNN),our method can reduce the loss of test model by 5.33%.And it’s an effective method in improving the robustness of translation and rotation information than traditional VO methods.展开更多
Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS mea...Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS measures in an attempt to provide a means for maintaining vehicle odometry during GPS outage. Nonetheless, recent experiments have demonstrated that computer vision can also be used as a valuable source to provide what can be denoted as visual odometry. For this purpose, vehicle motion can be estimated using a non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The results prove that the detection and selection of relevant feature points is a crucial factor in the global performance of the visual odometry algorithm. The key issues for further improvement are discussed in this letter.展开更多
Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly dist...Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.展开更多
In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error acc...In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point clouds.Iterative closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.展开更多
Robust and efficient vision systems are essential in such a way to support different kinds of autonomous robotic behaviors linked to the capability to interact with the surrounding environment, without relying on any ...Robust and efficient vision systems are essential in such a way to support different kinds of autonomous robotic behaviors linked to the capability to interact with the surrounding environment, without relying on any a priori knowledge. Within space missions, above all those involving rovers that have to explore planetary surfaces, vision can play a key role in the improvement of autonomous navigation functionalities： besides obstacle avoidance and hazard detection along the traveling, vision can in fact provide accurate motion estimation in order to constantly monitor all paths executed by the rover. The present work basically regards the development of an effective visual odometry system, focusing as much as possible on issues such as continuous operating mode, system speed and reliability.展开更多
目的基于VOSviewer软件可视化分析肾癌靶向治疗的研究现状、热点和前沿。方法从Web of Science(WOS)核心合集数据库中检索2006年1月1日—2023年12月31日发表的有关肾癌靶向治疗的文献,筛选出符合标准的文献,通过VOSviewer软件对文献进...目的基于VOSviewer软件可视化分析肾癌靶向治疗的研究现状、热点和前沿。方法从Web of Science(WOS)核心合集数据库中检索2006年1月1日—2023年12月31日发表的有关肾癌靶向治疗的文献,筛选出符合标准的文献,通过VOSviewer软件对文献进行计量分析和可视化分析。结果筛选后共获得1009篇文献,年发文量总体呈上升趋势;发文量排名前3位的国家依次是美国、中国和意大利,发文量排名前3位的机构依次是哈佛大学、得克萨斯大学和法国综合癌症中心。核心作者合作网络分析结果显示,美国和英国的研究人员在该领域的合作较为紧密,而中国与国外的合作较少,合作网络比较松散。选取高频作者关键词进行共现聚类分析,共生成9个聚类,热点主要集中于靶向与免疫联合治疗、疗效、预后、耐药性、靶点及生物标志物等方面。结论近十几年来,肾癌的靶向治疗研究取得显著进展,然而,靶向药物的耐药性和不良反应仍然是临床治疗中的难点。针对靶向药物耐药机制、新型靶向药物及有效预测生物标志物的相关研究显著增加。掌握该领域的发展趋势至关重要,VOSviewer可视化分析可展现该领域的现状、热点及前沿,为研究者提供直观的参考依据。展开更多
In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camer...In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.展开更多
目的 基于文献分析癌性疼痛(癌痛)药物研究热点和趋势。方法 检索在中国知网(CNKI)和Web of Science数据库发表的癌痛与药物相关研究文献,采用VOS-viewer 1.6.18版和CiteSpace 6.2.R3版软件对所纳入文献绘制图谱,对发文量、期刊、机构...目的 基于文献分析癌性疼痛(癌痛)药物研究热点和趋势。方法 检索在中国知网(CNKI)和Web of Science数据库发表的癌痛与药物相关研究文献,采用VOS-viewer 1.6.18版和CiteSpace 6.2.R3版软件对所纳入文献绘制图谱,对发文量、期刊、机构、作者和关键词进行分析。结果 共纳入文献4 774篇,发文量最多的期刊是《中国疼痛医学杂志》和《Journal of Pain and Symptom Management》;发文量最多的作者是刘端祺和Bruera Eduardo;发文量最高的机构为华中科技大学同济医学院附属同济医院和University of Texas Anderson Cancercenter;出现频次最高的关键词为癌痛;最新的突现词主要是临床疗效、日均费用和初级护理等。结论 进入21世纪,癌痛药物研究逐步升温,发文量呈上升趋势;2019-2022年,癌痛药物的研究国内外主要集中在癌痛药物的临床疗效、日均费用等方面。该文在一定程度上明确了癌痛药物近年来的研究热点,对今后的研究有一定参考意义。展开更多
基金supported by the National Natural Science Foundation of China (No.62202137)the China Postdoctoral Science Foundation (No.2023M730599)the Zhejiang Provincial Natural Science Foundation of China (No.LMS25F020009)。
文摘Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
基金the National Natural Science Foundation of China(No.62063006)the Natural Science Foundation of Guangxi Province(No.2023GXNS-FAA026025)+3 种基金the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005)the Research Project for Young andMiddle-Aged Teachers in Guangxi Universi-ties(ID:2020KY15013)the Special Research Project of Hechi University(ID:2021GCC028)financially supported by the Project of Outstanding Thousand Young Teachers’Training in Higher Education Institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region.
文摘Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.
文摘地热作为绿色低碳的可再生能源,在能源领域具有重要意义。为了探究当前国内外学者在地热领域的研究方向和热点,明确地热研究的发展前景,对2000—2023年中国知网(CNKI)和Web of Science(WoS)数据库所收录的地热领域高质量文献进行检索,并利用VOS viewer进行可视化分析,掌握数据库年发文量、国家和机构、期刊发文量、高被引文献及文献所涉及的关键词。研究结果表明,在2000—2023年间,地热研究方向中英文文献的发文量总体均呈上升趋势;在发文机构方面,中国地质大学、中国科学院等中国科研机构已跃升为发文的重要基石,主要发表在《水文地质工程地质》《Geothermics》《太阳能学报》《Geothermics》的文献最多,为1213篇;地热领域研究主要集中于地热资源勘探与开发。研究可为科研人员全面了解2000年至今的地热研究现状与未来发展趋势提供参考。
基金supported by National Key R&D Plan(2017YFB1301104),NSFC(61877040,61772351)Sci-Tech Innovation Fundamental Scientific Research Funds(025195305000)(19210010005),academy for multidisciplinary study of Capital Normal University。
文摘Error or drift is frequently produced in pose estimation based on geometric"feature detection and tracking"monocular visual odometry(VO)when the speed of camera movement exceeds 1.5 m/s.While,in most VO methods based on deep learning,weight factors are in the form of fixed values,which are easy to lead to overfitting.A new measurement system,for monocular visual odometry,named Deep Learning Visual Odometry(DLVO),is proposed based on neural network.In this system,Convolutional Neural Network(CNN)is used to extract feature and perform feature matching.Moreover,Recurrent Neural Network(RNN)is used for sequence modeling to estimate camera’s 6-dof poses.Instead of fixed weight values of CNN,Bayesian distribution of weight factors are introduced in order to effectively solve the problem of network overfitting.The 18,726 frame images in KITTI dataset are used for training network.This system can increase the generalization ability of network model in prediction process.Compared with original Recurrent Convolutional Neural Network(RCNN),our method can reduce the loss of test model by 5.33%.And it’s an effective method in improving the robustness of translation and rotation information than traditional VO methods.
文摘Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS measures in an attempt to provide a means for maintaining vehicle odometry during GPS outage. Nonetheless, recent experiments have demonstrated that computer vision can also be used as a valuable source to provide what can be denoted as visual odometry. For this purpose, vehicle motion can be estimated using a non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The results prove that the detection and selection of relevant feature points is a crucial factor in the global performance of the visual odometry algorithm. The key issues for further improvement are discussed in this letter.
基金Supported by Tianjin Municipal Natural Science Foundation of China(Grant No.19JCJQJC61600)Hebei Provincial Natural Science Foundation of China(Grant Nos.F2020202051,F2020202053).
文摘Visual odometry is critical in visual simultaneous localization and mapping for robot navigation.However,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive weight.Herein,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line features.Compared with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc datasets.Although the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time demand.The proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.
文摘In this paper,we present a novel algorithm for odometry estimation based on ceiling vision.The main contribution of this algorithm is the introduction of principal direction detection that can greatly reduce error accumulation problem in most visual odometry estimation approaches.The principal direction is defned based on the fact that our ceiling is flled with artifcial vertical and horizontal lines which can be used as reference for the current robot s heading direction.The proposed approach can be operated in real-time and it performs well even with camera s disturbance.A moving low-cost RGB-D camera（Kinect）,mounted on a robot,is used to continuously acquire point clouds.Iterative closest point（ICP） is the common way to estimate the current camera position by registering the currently captured point cloud to the previous one.However,its performance sufers from data association problem or it requires pre-alignment information.The performance of the proposed principal direction detection approach does not rely on data association knowledge.Using this method,two point clouds are properly pre-aligned.Hence,we can use ICP to fne-tune the transformation parameters and minimize registration error.Experimental results demonstrate the performance and stability of the proposed system under disturbance in real-time.Several indoor tests are carried out to show that the proposed visual odometry estimation method can help to signifcantly improve the accuracy of simultaneous localization and mapping（SLAM）.
文摘Robust and efficient vision systems are essential in such a way to support different kinds of autonomous robotic behaviors linked to the capability to interact with the surrounding environment, without relying on any a priori knowledge. Within space missions, above all those involving rovers that have to explore planetary surfaces, vision can play a key role in the improvement of autonomous navigation functionalities： besides obstacle avoidance and hazard detection along the traveling, vision can in fact provide accurate motion estimation in order to constantly monitor all paths executed by the rover. The present work basically regards the development of an effective visual odometry system, focusing as much as possible on issues such as continuous operating mode, system speed and reliability.
文摘目的基于VOSviewer软件可视化分析肾癌靶向治疗的研究现状、热点和前沿。方法从Web of Science(WOS)核心合集数据库中检索2006年1月1日—2023年12月31日发表的有关肾癌靶向治疗的文献,筛选出符合标准的文献,通过VOSviewer软件对文献进行计量分析和可视化分析。结果筛选后共获得1009篇文献,年发文量总体呈上升趋势;发文量排名前3位的国家依次是美国、中国和意大利,发文量排名前3位的机构依次是哈佛大学、得克萨斯大学和法国综合癌症中心。核心作者合作网络分析结果显示,美国和英国的研究人员在该领域的合作较为紧密,而中国与国外的合作较少,合作网络比较松散。选取高频作者关键词进行共现聚类分析,共生成9个聚类,热点主要集中于靶向与免疫联合治疗、疗效、预后、耐药性、靶点及生物标志物等方面。结论近十几年来,肾癌的靶向治疗研究取得显著进展,然而,靶向药物的耐药性和不良反应仍然是临床治疗中的难点。针对靶向药物耐药机制、新型靶向药物及有效预测生物标志物的相关研究显著增加。掌握该领域的发展趋势至关重要,VOSviewer可视化分析可展现该领域的现状、热点及前沿,为研究者提供直观的参考依据。
基金Supported by the National Natural Science Foundation of China(61501034)
文摘In this paper a semi-direct visual odometry and mapping system is proposed with a RGB-D camera,which combines the merits of both feature based and direct based methods.The presented system directly estimates the camera motion of two consecutive RGB-D frames by minimizing the photometric error.To permit outliers and noise,a robust sensor model built upon the t-distribution and an error function mixing depth and photometric errors are used to enhance the accuracy and robustness.Local graph optimization based on key frames is used to reduce the accumulative error and refine the local map.The loop closure detection method,which combines the appearance similarity method and spatial location constraints method,increases the speed of detection.Experimental results demonstrate that the proposed approach achieves higher accuracy on the motion estimation and environment reconstruction compared to the other state-of-the-art methods. Moreover,the proposed approach works in real-time on a laptop without a GPU,which makes it attractive for robots equipped with limited computational resources.
文摘目的 基于文献分析癌性疼痛(癌痛)药物研究热点和趋势。方法 检索在中国知网(CNKI)和Web of Science数据库发表的癌痛与药物相关研究文献,采用VOS-viewer 1.6.18版和CiteSpace 6.2.R3版软件对所纳入文献绘制图谱,对发文量、期刊、机构、作者和关键词进行分析。结果 共纳入文献4 774篇,发文量最多的期刊是《中国疼痛医学杂志》和《Journal of Pain and Symptom Management》;发文量最多的作者是刘端祺和Bruera Eduardo;发文量最高的机构为华中科技大学同济医学院附属同济医院和University of Texas Anderson Cancercenter;出现频次最高的关键词为癌痛;最新的突现词主要是临床疗效、日均费用和初级护理等。结论 进入21世纪,癌痛药物研究逐步升温,发文量呈上升趋势;2019-2022年,癌痛药物的研究国内外主要集中在癌痛药物的临床疗效、日均费用等方面。该文在一定程度上明确了癌痛药物近年来的研究热点,对今后的研究有一定参考意义。
