Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

Automatic Feature-Based Stabilization of Video with Intentional Motion through a Particle Filter

Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorith

Gaussian process for ground-motion prediction and emulation of systems of computer models

In this thesis, several challenges in both ground-motion modelling and the surrogate modelling, are addressed by developing methods based on Gaussian processes (GP). The first chapter contains an overview of the GP and summarises the key findings of the rest of the thesis. In the second chapter, an estimation algorithm, called the Scoring estimation approach, is developed to train GP-based ground-motion models with spatial correlation. The Scoring estimation approach is introduced theoretically and numerically, and it is proven to have desirable properties on convergence and computation. It is a statistically robust method, producing consistent and statistically efficient estimators of spatial correlation parameters. The predictive performance of the estimated ground-motion model is assessed by a simulation-based application, which gives important implications on the seismic risk assessment. In the third chapter, a GP-based surrogate model, called the integrated emulator, is introduced to emulate a system of multiple computer models. It generalises the state-of-the-art linked emulator for a system of two computer models and considers a variety of kernels (exponential, squared exponential, and two key Matérn kernels) that are essential in advanced applications. By learning the system structure, the integrated emulator outperforms the composite emulator, which emulates the entire system using only global inputs and outputs. Furthermore, its analytic expressions allow a fast and efficient design algorithm that could yield significant computational and predictive gains by allocating different runs to individual computer models based on their heterogeneous functional complexity. The benefits of the integrated emulator are demonstrated in a series of synthetic experiments and a feed-back coupled fire-detection satellite model. Finally, the developed method underlying the integrated emulator is used to construct a non-stationary Gaussian process model based on deep Gaussian hierarchy

Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.Comment: Accepted by ICRA 201