Akbar Assa

A Robust Vision-Based Sensor Fusion Approach for Real-Time Pose Estimation

Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.

Virtual Visual Servoing for Multicamera Pose Estimation

Exploiting multiple cameras for object pose estimation contributes toward achieving enhanced accuracy and robustness to measurement faultiness. Nonlinear optimization frameworks, such as the Gauss-Newton, were traditionally employed for this purpose. This paper proposes a novel algorithm for multicamera pose estimation by using the concept of virtual visual servoing. Two fusion structures, namely centralized and decentralized fusion, are considered. The former offers higher accuracy at the price of increased computation. The latter improves the computation speed, partially sacrificing the system accuracy. The experimental results imply the proposed configurations to be faster than the Gauss-Newton pose estimation method, while offering the same or even higher level of accuracy.

A Kalman Filter-Based Framework for Enhanced Sensor Fusion

Sensor fusion has found a lot of applications in todays industrial and scientific world with Kalman filtering being one of the most practiced methods. Despite their simplicity and effectiveness, Kalman filters are usually prone to uncertainties in system parameters and particularly system noise covariance. This paper proposes a Kalman filtering framework for sensor fusion, which provides robustness to the uncertainties in the system parameters such as noise covariance and state initialization. Two methods are developed based on the proposed approach. The effectiveness of the proposed methods is verified through numerous simulations and experiments.

Hybrid predictive control for constrained visual servoing

Visual servoing provides a smart solution for robot control in unstructured environments. However, exploiting camera images for control imposes additional constraints to the system. Previously image-based predictive controllers were proposed as a remedy for this problem. Nevertheless, these methods suffered from local stability and possible numerical infeasibility. This work presents two hybrid predictive controllers that are globally stable and have minimum computation time. One of the controllers is based on hybrid visual servoing, while the other minimizes the error in both image and Cartesian space simultaneously with selected ratios. The effectiveness of the proposed methods is verified through numerous simulations.

Robust model predictive control for visual servoing

Visual servoing methods have proven their usefulness for robot control in unstructured environments. However, the practicality of such methods highly depends on their robustness to system uncertainties and ability to handle the constraints of the system. Many of the previous works proposed effective remedies for constraint handling; yet, only a few of them considered the system uncertainties. This work proposes a novel control scheme for visual servoing which basically exploits a model predictive controller to handle the constraints. In addition to that, the uncertainty model of the system is developed to handle the constraints more efficiently. The simulation and experimental results confirm the effectiveness of the proposed control method for constraint handling, in the presence of system uncertainties.

Closed-loop uncertainty modeling for visual servoing

Robustness is the key feature for practical visual servoing systems. Uncertainty modeling in these systems is the first step towards accurate and robust visual servoing. Camera displacement error due to image noise has been investigated using the noise boundaries in the previous works. Also error covariance propagation has already been developed to model the uncertainties of different visual servoing system components. However, these works were either open-loop or not fully successful under controlled simulations. This work presents a new error modeling method based on error covariance propagation in closed-loop visual servoing systems, considering the discrete-time nature of the system. The proposed framework could be employed for a broad range of controllers. Simulation results suggest the effectiveness of the proposed method.

A data fusion approach for multi-camera based visual servoing

Accurate visual servoing depends extensively on the quality of pose estimation. Sensor fusion provides a solution to improve accuracy and robustness of pose estimation. This paper introduces sensor fusion methods using two cameras to reduce the inaccuracy of pose estimation. Simulation results are reported to verify the efficiency of the proposed methods.

Decentralized multi-camera fusion for robust and accurate pose estimation

Object pose estimation in Cartesian space has found a fundamental role is various applications such as object recognition and visual servoing. Performance and reliability of many of these applications are highly dependent on accuracy and robustness of pose estimation. While monocular systems are usually sufficient for this purpose, multi-camera configurations offer enhanced accuracy and field of view (FOV). Sensor fusion is one smart strategy to exploit data from multiple cameras. This work presents a decentralized sensor fusion scheme which is fault tolerant and does not demand external camera or robot calibrations. Experimental and Simulation data are provided to verify the effectiveness of this scheme.

Novel two-stage control scheme for robust constrained visual servoing

Visual servoing techniques are proven to be beneficial in unstructured workspaces. However, visual servoing is bounded by several constraints and is prone to the uncertainties of the system, leaving it of limited applicability. Several previous works have tackled these problems; yet, most of these works considered only a partial set of the aforementioned shortcomings. This work proposes a novel two-stage controller that is capable of minimizing the uncertainties of the system, while handling the constraints properly. The effectiveness of the controller is put into test via various simulations and experiments. The experimental results confirm the usefulness and applicability of this controller.

Two DOF controller for decoupled image-based visual servoing

Robotic manipulators have found wide applications in various industries such as automotive industry and aerospace. Successful robot manipulation usually requires a highly structured workspace, which is expensive and time consuming. Visual servoing offers an interesting remedy to this problem by exploiting camera images for robot control. Typically, a single degree of freedom proportional controller is employed for servoing, which has to compromise between uncertainties and rate of convergence. This work proposes a two degree of freedom controller to address this problem. The proposed controller separates the rate of convergence from the uncertainty, does not require online depth estimation, and is faster compared to classic proportional controller. The effectiveness of the proposed controller is verified through simulations.