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
We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system(OTS). By data fusion with an inertia...We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system(OTS). By data fusion with an inertial measurement unit(IMU), both robustness and accuracy of OTS are improved. The Kalman filter is used in data fusion. The error distribution of OTS pro-vides an important reference on the estimation of measurement noise using the Kalman filter. With a proper setup of the system and an effective method of coordinate frame synchronization, the results of experiments show a significant improvement in terms of robustness and position accuracy.展开更多
Motion tracking via Inertial Measurement Units(IMUs)on mobile and wearable devices has attracted significant interest in recent years.High-accuracy IMU-tracking can be applied in various applications,such as indoor na...Motion tracking via Inertial Measurement Units(IMUs)on mobile and wearable devices has attracted significant interest in recent years.High-accuracy IMU-tracking can be applied in various applications,such as indoor navigation,gesture recognition,text input,etc.Many efforts have been devoted to improving IMU-based motion tracking in the last two decades,from early calibration techniques on ships or airplanes,to recent arm motion models used on wearable smart devices.In this paper,we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices.We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.展开更多
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Project supported by the National Natural Science Foundation of China(No.51221004)Sponsored Research between ABB Re-search Ltd. and Zhejiang University
文摘We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system(OTS). By data fusion with an inertial measurement unit(IMU), both robustness and accuracy of OTS are improved. The Kalman filter is used in data fusion. The error distribution of OTS pro-vides an important reference on the estimation of measurement noise using the Kalman filter. With a proper setup of the system and an effective method of coordinate frame synchronization, the results of experiments show a significant improvement in terms of robustness and position accuracy.
基金part supported by the National Key R&D Program of China(No.2018YFB1004800)the National Natural Science Foundation of China(No.61932013)。
文摘Motion tracking via Inertial Measurement Units(IMUs)on mobile and wearable devices has attracted significant interest in recent years.High-accuracy IMU-tracking can be applied in various applications,such as indoor navigation,gesture recognition,text input,etc.Many efforts have been devoted to improving IMU-based motion tracking in the last two decades,from early calibration techniques on ships or airplanes,to recent arm motion models used on wearable smart devices.In this paper,we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices.We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.
