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
In order to improve lesion localisation in small-bowel capsule endoscopy,a modified capsule design has been proposed incorporating localisation and-in theorystabilization capabilities.The proposed design consists of a...In order to improve lesion localisation in small-bowel capsule endoscopy,a modified capsule design has been proposed incorporating localisation and-in theorystabilization capabilities.The proposed design consists of a capsule fitted with protruding wheels attached to a spring-mechanism.This would act as a miniature odometer,leading to more accurate lesion localization information in relation to the onset of the investigation(spring expansion e.g.,pyloric opening).Furthermore,this capsule could allow stabilization of the recorded video as any erratic,non-forward movement through the gut is minimised.Three-dimensional(3-D)printing technology was used to build a capsule prototype.Thereafter,miniature wheels were also 3-D printed and mounted on a spring which was attached to conventional capsule endoscopes for the purpose of this proof-of-concept experiment.In vitro and ex vivo experiments with porcine small-bowel are presented herein.Further experiments have been scheduled.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
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Supported by SynMed UK related to this workDr.Koulaouzidis A has also received lecture honoraria from Dr Falk PharmaUnited kingdom
文摘In order to improve lesion localisation in small-bowel capsule endoscopy,a modified capsule design has been proposed incorporating localisation and-in theorystabilization capabilities.The proposed design consists of a capsule fitted with protruding wheels attached to a spring-mechanism.This would act as a miniature odometer,leading to more accurate lesion localization information in relation to the onset of the investigation(spring expansion e.g.,pyloric opening).Furthermore,this capsule could allow stabilization of the recorded video as any erratic,non-forward movement through the gut is minimised.Three-dimensional(3-D)printing technology was used to build a capsule prototype.Thereafter,miniature wheels were also 3-D printed and mounted on a spring which was attached to conventional capsule endoscopes for the purpose of this proof-of-concept experiment.In vitro and ex vivo experiments with porcine small-bowel are presented herein.Further experiments have been scheduled.
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/.
