Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-...Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.展开更多
Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy ...Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy of the estimation results and the robustness of VIO. In high contrast lighting condition environment, images captured by auto exposure camera shows frequently change with its exposure time. As a result, the gray value of the same feature in the image show vary from frame to frame, which poses large challenge to the feature detection and tracking procedure. Moreover, this problem further been aggravated by the nonlinear camera response function and lens attenuation. However, very few VIO methods take full advantage of photometric camera calibration and discuss the influence of photometric calibration to the VIO. In this paper, we proposed a robust monocular visual-inertial odometry, PC-VINS-Mono, which can be understood as an extension of the opens-source VIO pipeline, VINS-Mono, with the capability of photometric calibration. We evaluate the proposed algorithm with the public dataset. Experimental results show that, with photometric calibration, our algorithm achieves better performance comparing to the VINS-Mono.展开更多
Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.Howeve...Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.However,VIOs usually have the degraded performance in challenging environments and degenerated motion scenarios.In this paper,we propose a ground vehicle-based VIO algorithm based on the Multi-State Constraint Kalman Filter(MSCKF)framework.Based on a unifed motion manifold assumption,we derive the measurement model of manifold constraints,including velocity,rotation,and translation constraints.Then we present a robust flter-based algorithm dedicated to ground vehicles,whose key is the real-time manifold noise estimation and adaptive measurement update.Besides,GNSS position measurements are loosely coupled into our approach,where the transformation between GNSS and VIO frame is optimized online.Finally,we theoretically analyze the system observability matrix and observability measures.Our algorithm is tested on both the simulation test and public datasets including Brno Urban dataset and Kaist Urban dataset.We compare the performance of our algorithm with classical VIO algorithms(MSCKF,VINS-Mono,R-VIO,ORB_SLAM3)and GVIO algorithms(GNSS-MSCKF,VINS-Fusion).The results demonstrate that our algorithm is more robust than other compared algorithms,showing a competitive position accuracy and computational efciency.展开更多
This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By inte...This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By integrating the optical flow method,the algorithm tracks both point and line features in images simultaneously,significantly reducing computational complexity and the matching time for line feature descriptors.Additionally,this paper advances the triangulation method for line features,using depth information from line segment endpoints to determine their Plcker coordinates in three-dimensional space.Tests on the EuRoC datasets show that the proposed algorithm outperforms PL-VIO in terms of processing speed per frame,with an approximate 5%to 10%improvement in both relative pose error(RPE)and absolute trajectory error(ATE).These results demonstrate that the proposed VIO algorithm is an efficient solution suitable for low-computing platforms requiring real-time localization and navigation.展开更多
Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an ...Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an appropriate benchmark.For AR applications in practice,a variety of challenging situations(e.g.,fast motion,strong rotation,serious motion blur,dynamic interference)may be easily encountered since a home user may not carefully move the AR device,and the real environment may be quite complex.In addition,the frequency of camera lost should be minimized and the recovery from the failure status should be fast and accurate for good AR experience.Existing SLAM datasets/benchmarks generally only provide the evaluation of pose accuracy and their camera motions are somehow simple and do not fit well the common cases in the mobile AR applications.With the above motivation,we build a new visual-inertial dataset as well as a series of evaluation criteria for AR.We also review the existing monocular VSLAM/VISLAM approaches with detailed analyses and comparisons.Especially,we select 8 representative monocular VSLAM/VISLAM approaches/systems and quantitatively evaluate them on our benchmark.Our dataset,sample code and corresponding evaluation tools are available at the benchmark website http://www.zjucvg.net/eval-vislam/.展开更多
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
Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance ...Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.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
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
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
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
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
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
针对基于线面特征的激光雷达里程计算法易导致表面特征信息的冗余表述、增加计算复杂度的问题,提出一种基于水平扫描线段结构的轻量化激光雷达里程计构建方法。首先通过分析激光点云扫描线几何结构,将点云特征分为线段特征、边特征和离...针对基于线面特征的激光雷达里程计算法易导致表面特征信息的冗余表述、增加计算复杂度的问题,提出一种基于水平扫描线段结构的轻量化激光雷达里程计构建方法。首先通过分析激光点云扫描线几何结构,将点云特征分为线段特征、边特征和离散特征,分别表示3维点云中物体的平面信息、边界信息和空间分布信息,以线段表示平面降低特征数量;然后基于历史位姿,采用运动估计方式获取初始位姿,通过非迭代的两步加权位姿估计算法进行特征配准与位姿解算;最后以提取关键帧方式存储点云,避免因点云地图过大造成匹配延时。在KITTI数据集和自研数据集上的实验表明,与现有的开源LOAM(LiDAR odometry and mapping)系列算法相比,本文算法在实现高精度稳定定位的基础上运行效率显著提升,且绝对轨迹误差抑制效果较好。展开更多
传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出...传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。展开更多
基金supported by the National Key Research and Development Program of China(Nos.2016YFB0502004,2017YFC0821102)。
文摘Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.
基金support from National Natural Science Foundation of China (No.61375086)Key Project (No.KZ201610005010) of S&T Plan of Beijing Municipal Commission of EducationBeijing Natural Science Foundation(4174083).
文摘Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy of the estimation results and the robustness of VIO. In high contrast lighting condition environment, images captured by auto exposure camera shows frequently change with its exposure time. As a result, the gray value of the same feature in the image show vary from frame to frame, which poses large challenge to the feature detection and tracking procedure. Moreover, this problem further been aggravated by the nonlinear camera response function and lens attenuation. However, very few VIO methods take full advantage of photometric camera calibration and discuss the influence of photometric calibration to the VIO. In this paper, we proposed a robust monocular visual-inertial odometry, PC-VINS-Mono, which can be understood as an extension of the opens-source VIO pipeline, VINS-Mono, with the capability of photometric calibration. We evaluate the proposed algorithm with the public dataset. Experimental results show that, with photometric calibration, our algorithm achieves better performance comparing to the VINS-Mono.
基金the National Nature Science Foundation of China(NSFC)under Grant No.62273229the Equipment PreResearch Field Foundation under Grant No.80913010303.
文摘Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.However,VIOs usually have the degraded performance in challenging environments and degenerated motion scenarios.In this paper,we propose a ground vehicle-based VIO algorithm based on the Multi-State Constraint Kalman Filter(MSCKF)framework.Based on a unifed motion manifold assumption,we derive the measurement model of manifold constraints,including velocity,rotation,and translation constraints.Then we present a robust flter-based algorithm dedicated to ground vehicles,whose key is the real-time manifold noise estimation and adaptive measurement update.Besides,GNSS position measurements are loosely coupled into our approach,where the transformation between GNSS and VIO frame is optimized online.Finally,we theoretically analyze the system observability matrix and observability measures.Our algorithm is tested on both the simulation test and public datasets including Brno Urban dataset and Kaist Urban dataset.We compare the performance of our algorithm with classical VIO algorithms(MSCKF,VINS-Mono,R-VIO,ORB_SLAM3)and GVIO algorithms(GNSS-MSCKF,VINS-Fusion).The results demonstrate that our algorithm is more robust than other compared algorithms,showing a competitive position accuracy and computational efciency.
文摘This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By integrating the optical flow method,the algorithm tracks both point and line features in images simultaneously,significantly reducing computational complexity and the matching time for line feature descriptors.Additionally,this paper advances the triangulation method for line features,using depth information from line segment endpoints to determine their Plcker coordinates in three-dimensional space.Tests on the EuRoC datasets show that the proposed algorithm outperforms PL-VIO in terms of processing speed per frame,with an approximate 5%to 10%improvement in both relative pose error(RPE)and absolute trajectory error(ATE).These results demonstrate that the proposed VIO algorithm is an efficient solution suitable for low-computing platforms requiring real-time localization and navigation.
基金the National Key Research and Development Program of China(2016YFB1001501)NSF of China(61672457)+1 种基金the Fundamental Research Funds for the Central Universities(2018FZA5011)Zhejiang University-SenseTime Joint Lab of 3D Vision.
文摘Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an appropriate benchmark.For AR applications in practice,a variety of challenging situations(e.g.,fast motion,strong rotation,serious motion blur,dynamic interference)may be easily encountered since a home user may not carefully move the AR device,and the real environment may be quite complex.In addition,the frequency of camera lost should be minimized and the recovery from the failure status should be fast and accurate for good AR experience.Existing SLAM datasets/benchmarks generally only provide the evaluation of pose accuracy and their camera motions are somehow simple and do not fit well the common cases in the mobile AR applications.With the above motivation,we build a new visual-inertial dataset as well as a series of evaluation criteria for AR.We also review the existing monocular VSLAM/VISLAM approaches with detailed analyses and comparisons.Especially,we select 8 representative monocular VSLAM/VISLAM approaches/systems and quantitatively evaluate them on our benchmark.Our dataset,sample code and corresponding evaluation tools are available at the benchmark website http://www.zjucvg.net/eval-vislam/.
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 MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NIPA-2012-C1090-1221-0010)TheMKE,Korea,under the Human Resources Development Programfor Convergence Robot Specialists support programsu-pervised by the NIPA(NIPA-2012-H1502-12-1002)Basic Science Research Program through the NRF funded by the MEST(2011-0025980)and MEST(2012-0005487)
文摘Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.
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.
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.
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.
文摘针对基于线面特征的激光雷达里程计算法易导致表面特征信息的冗余表述、增加计算复杂度的问题,提出一种基于水平扫描线段结构的轻量化激光雷达里程计构建方法。首先通过分析激光点云扫描线几何结构,将点云特征分为线段特征、边特征和离散特征,分别表示3维点云中物体的平面信息、边界信息和空间分布信息,以线段表示平面降低特征数量;然后基于历史位姿,采用运动估计方式获取初始位姿,通过非迭代的两步加权位姿估计算法进行特征配准与位姿解算;最后以提取关键帧方式存储点云,避免因点云地图过大造成匹配延时。在KITTI数据集和自研数据集上的实验表明,与现有的开源LOAM(LiDAR odometry and mapping)系列算法相比,本文算法在实现高精度稳定定位的基础上运行效率显著提升,且绝对轨迹误差抑制效果较好。
文摘传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。
