Real-Time 3D Motion Tracking and Reconstruction System Using Camera and IMU Sensors

Li, Changdi; Yu, Lei; Fei, Shumin

doi:10.1109/jsen.2019.2907716

Cited by 42 publications

(11 citation statements)

References 20 publications

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“…Note that the whole algorithm is performed in real time. Therefore, it could be applied to many online vision tasks, such as real-time 3D reconstruction [9,10,[20][21][22].…”

Section: Capementioning

confidence: 99%

Real-Time Plane Detection with Consistency from Point Cloud Sequences

Xie

Chen

et al. 2020

Sensors

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Real-time consistent plane detection (RCPD) from structured point cloud sequences facilitates various high-level computer vision and robotic tasks. However, it remains a challenge. Existing techniques for plane detection suffer from a long running time or the problem that the plane detection result is not precise. Meanwhile, labels of planes are not consistent over the whole image sequence due to plane loss in the detection stage. In order to resolve these issues, we propose a novel superpixel-based real-time plane detection approach, while keeping their consistencies over frames simultaneously. In summary, our method has the following key contributions: (i) a real-time plane detection algorithm to extract planes from raw structured three-dimensional (3D) point clouds collected by depth sensors; (ii) a superpixel-based segmentation method to make the detected plane exactly match its actual boundary; and, (iii) a robust strategy to recover the missing planes by utilizing the contextual correspondences information in adjacent frames. Extensive visual and numerical experiments demonstrate that our method outperforms state-of-the-art methods in terms of efficiency and accuracy.

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“…Note that the whole algorithm is performed in real time. Therefore, it could be applied to many online vision tasks, such as real-time 3D reconstruction [9,10,[20][21][22].…”

Section: Capementioning

confidence: 99%

Real-Time Plane Detection with Consistency from Point Cloud Sequences

Xie

Chen

et al. 2020

Sensors

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“…7 However, such huge robots will cause serious problems if they do not work in the right pattern. Some articles combine camera and inertial measurement unit (IMU) together to correct the error caused only by IMU, 8,9 which shows that camera has similar function as IMU that can correct or even replace IMU module. All errors in this article mean absolute error, which is defined by the difference between the expected value and the true value.…”

Section: Introductionmentioning

confidence: 99%

Industry robotic motion and pose recognition method based on camera pose estimation and neural network

Wang

Xie

Yang

et al. 2021

International Journal of Advanced Robotic Systems

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To control industry robots and make sure they are working in a correct status, an efficient way to judge the motion of the robot is important. In this article, an industry robotic motion and pose recognition method based on camera pose estimation and neural network are proposed. Firstly, industry robotic motion recognition based on the neural network has been developed to estimate and optimize motion of the robotics only by a monoscope camera. Secondly, the motion recognition including key flames recording and pose adjustment has been proposed and analyzed to restore the pose of the robotics more accurately. Finally, a KUKA industry robot has been used to test the proposed method, and the test results have demonstrated that the motion and pose recognition method can recognize the industry robotic pose accurately and efficiently without inertial measurement unit (IMU) and other censers. Below in the same algorithm, the error of the method introduced in this article is better than the traditional method using IMU and has a better merit of reducing cumulative error.

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“…You et al [ 7 ] used Unscented Kalman filter to fuse UWB and IMU data for the localization of quadrotor UAV in indoor environments. Li et al [ 8 ] employed sliding window filter (SWF) to fuse camera and IMU data for accurate 3D motion tracking and reconstruction. Peng et al [ 9 ] proposed a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating, which could fuse multiple kinds of sensors data.…”

Section: Introductionmentioning

confidence: 99%

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

Liu

Wang

et al. 2020

Sensors

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In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

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Real-Time 3D Motion Tracking and Reconstruction System Using Camera and IMU Sensors

Cited by 42 publications

References 20 publications

Real-Time Plane Detection with Consistency from Point Cloud Sequences

Real-Time Plane Detection with Consistency from Point Cloud Sequences

Industry robotic motion and pose recognition method based on camera pose estimation and neural network

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

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