In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multip...In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multiple small-scale underwater robots. Therefore, this paper proposes a novel RGB-D camera and Inertial Measurement Unit (IMU) fusion-based cooperative and relative close-range localization approach for special environments, such as underwater caves. Owing to the rotation movement with zero-radius, the cooperative localization of Multiple Turtle-inspired Amphibious Spherical Robot (MTASRs) is realized. Firstly, we present an efficient Histogram of Oriented Gradient (HOG) and Color Names (CNs) fusion feature extracted from color images ofTASRs. Then, by training Support Vector Machine (SVM) classifier with this fusion feature, an automatic recognition method of TASRs is developed. Secondly, RGB-D camerabased measurement model is obtained by the depth map In order to realize the cooperative and relative close-range localization of MTASRs, the MTASRs model is established with RGB-D camera and IMU. Finally, the depth measurement in water is corrected and the efficiency of RGB-D camera for underwater application is validated. Then experiments of our proposed localization method with three robots were conducted and the results verified the feasibility of the proposed method for MTASRs.展开更多
Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance o...Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.展开更多
Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,the...Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,there has not been a complete review on image-based camera localization.It is urgent to map this topic to enable individuals enter the field quickly.In this paper,an overview of image-based camera localization is presented.A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced.Trends for future development are also discussed.This will be useful not only to researchers,but also to engineers and other individuals interested in this field.展开更多
基金the National Natural Science Foundation of China (Nos. 61773064, 61503028)Graduate Technological Innovation Project of Beijing Institute of Technology (No. 2018CX10022)National High Tech. Research and Development Program of China (No. 2015AA043202).
文摘In the narrow, submarine, unstructured environment, the present localization approaches, such as GPS measurement, dead?rcckoning, acoustic positioning, artificial landmarks-based method, are hard to be used for multiple small-scale underwater robots. Therefore, this paper proposes a novel RGB-D camera and Inertial Measurement Unit (IMU) fusion-based cooperative and relative close-range localization approach for special environments, such as underwater caves. Owing to the rotation movement with zero-radius, the cooperative localization of Multiple Turtle-inspired Amphibious Spherical Robot (MTASRs) is realized. Firstly, we present an efficient Histogram of Oriented Gradient (HOG) and Color Names (CNs) fusion feature extracted from color images ofTASRs. Then, by training Support Vector Machine (SVM) classifier with this fusion feature, an automatic recognition method of TASRs is developed. Secondly, RGB-D camerabased measurement model is obtained by the depth map In order to realize the cooperative and relative close-range localization of MTASRs, the MTASRs model is established with RGB-D camera and IMU. Finally, the depth measurement in water is corrected and the efficiency of RGB-D camera for underwater application is validated. Then experiments of our proposed localization method with three robots were conducted and the results verified the feasibility of the proposed method for MTASRs.
文摘Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.
文摘针对非结构化场景中无人驾驶车辆定位误差大的问题,结合车载激光雷达和路侧双目摄像头,采用双层融合协同定位算法实现高精度定位.下层包含2个并行位姿估计,基于双地图的自适应蒙特卡洛定位,根据位姿偏差的短期和长期估计实现双地图切换,修正激光雷达扫描匹配的累积误差;基于概率数据关联的卡尔曼滤波位姿估计,消除非检测目标对路侧摄像头的干扰,实现目标跟踪.上层作为全局融合估计,融合下层的2个位姿估计,利用反馈实现自主调节.实车实验表明,双层融合协同定位的定位精度为0.199 m,航向角精度为2.179°,相比车载激光雷达定位和无反馈的紧融合定位有大幅提升;随着路侧摄像头数量的增加,定位精度可以达到7.8 cm.
基金supported by the National Natural Science Foundation of China under Grant No.61421004,61572499,61632003.
文摘Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,there has not been a complete review on image-based camera localization.It is urgent to map this topic to enable individuals enter the field quickly.In this paper,an overview of image-based camera localization is presented.A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced.Trends for future development are also discussed.This will be useful not only to researchers,but also to engineers and other individuals interested in this field.
