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This paper proposes a method to measure the motion of a moving rigid body using a hybrid visual–inertial sensor. The rotational velocity of the moving object is computed from visual optical flow by solving a depth-independent bilinear constraint, and the translational velocity of the moving object is estimated by solving a dynamics constraint that reveals the relation between scene depth and translational motion. By fusing an inertial sensor, the scale of translational velocities can be estimated, which is otherwise unrecoverable from monocular visual optical flow. An iterative refinement scheme is introduced to deal with observation noise and outliers, and the extended Kalman filter is applied for motion tracking. The performance of the proposed method is evaluated by simulation studies and practical experiments, and the results show the effectiveness of the proposed method in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive absolute ego-motion estimation method using wearable visual-inertial sensors for indoor positioning. We introduce a wearable visual-inertial device to estimate not only the camera ego-motion, but also the 3D motion of the moving object in dynamic environments. Firstly, a novel method dynamic scene segmentation is proposed using two visual geometry constraints with the help of inertial sensors. Moreover, this paper introduces a concept of "virtual camera" to consider the motion area related to each moving object as if a static object were viewed by a "virtual camera". We therefore derive the 3D moving object's motion from the motions for the real and virtual camera because the virtual camera's motion is actually the combined motion of both the real camera and the moving object. In addition, a multi-rate linear Kalman-filter (MR-LKF) as our previous work was selected to solve both the problem of scale ambiguity in monocular camera tracking and the different sampling frequencies of visual and inertial sensors. The performance of the proposed method is evaluated by simulation studies and practical experiments performed in both static and dynamic environments. The results show the method's robustness and effectiveness compared with the results from a Pioneer robot as the ground truth. [ABSTRACT FROM AUTHOR]

This paper proposes an ego-motion tracking method that utilizes visual-inertial sensors for wearable blind navigation. The unique challenge of wearable motion tracking is to cope with arbitrary body motion and complex environmental dynamics. We introduce a visual sanity check to select accurate visual estimations by comparing visually estimated rotation with measured rotation by a gyroscope. The movement trajectory is recovered through adaptive fusion of visual estimations and inertial measurements, where the visual estimation outputs motion transformation between consecutive image captures, and inertial sensors measure translational acceleration and angular velocities. The frame rates of visual and inertial sensors are different, and vary with respect to time owning to visual sanity checks. We hence employ a multirate extended Kalman filter (EKF) to fuse visual and inertial estimations. The proposed method was tested in different indoor environments, and the results show its effectiveness and accuracy in ego-motion tracking. [ABSTRACT FROM AUTHOR]

This paper presents a novel visual-inertial integration system for human navigation in free-living environments, where the measurements from wearable inertial and monocular visual sensors are integrated. The preestimated orientation, obtained from magnet, angular rate, and gravity sensors, is used to estimate the translation based on the data from the visual and inertial sensors. This has a significant effect on the performance of the fusion sensing strategy and makes the fusion procedure much easier, because the gravitational acceleration can be correctly removed from the accelerometer measurements before the fusion procedure, where a linear Kalman filter is selected as the fusion estimator. Furthermore, the use of preestimated orientation can help to eliminate erroneous point matches based on the properties of the pure camera translation and thus the computational requirements can be significantly reduced compared with the RANdom SAmple Consensus algorithm. In addition, an adaptive-frame rate single camera is selected to not only avoid motion blur based on the angular velocity and acceleration after compensation, but also to make an effect called visual zero-velocity update for the static motion. Thus, it can recover a more accurate baseline and meanwhile reduce the computational requirements. In particular, an absolute scale factor, which is usually lost in monocular camera tracking, can be obtained by introducing it into the estimator. Simulation and experimental results are presented for different environments with different types of movement and the results from a Pioneer robot are used to demonstrate the accuracy of the proposed method. [ABSTRACT FROM PUBLISHER]

Nuts and bolts are common components in assembly lines. Their position and pose estimation is a vital step for automatic assembling. Although many approaches using a monocular camera have been proposed, few works consider a monocular camera’s active movements for improving estimation accuracy. This article presents an active movement strategy for a monocular eye-in-hand camera for high position and pose estimation accuracy of a spatial circle. Extensive experiments are conducted to validate the effectiveness of the proposed method for position and pose estimation of circles printed on paper, real circular flat washers, and nuts. [ABSTRACT FROM AUTHOR]