This paper proposes a real-time, robust and effective tracking framework for visual servoing applications. The algorithm is based on the fusion of visual cues and on the estimation of a transformation (either a homography or a 3D pose). The parameters of this transformation are estimated using a non-linear minimization of a unique criterion that integrates information both on the texture and the edges of the tracked object. The proposed tracker is more robust and performs well in conditions where methods based on a single cue fail. The framework has been tested for 2D object motion estimation and pose computation. The method presented in this paper has been validated on several video sequences as well as in visual servoing experiments considering various objects. Results show the method to be robust to occlusions or textured backgrounds and suitable for visual servoing applications.

In this paper we present a direct tracking approach that uses Mutual Information (MI) as a metric for alignment. The proposed approach is robust, real-time and gives an accurate estimation of the displacement that makes it adapted to augmented reality applications. MI is a measure of the quantity of information shared by signals that has been widely used in medical applications. Since then, and although MI has the ability to perform robust alignment with illumination changes, multi-modality and partial occlusions, few works propose MI-based applications related to object tracking in image sequences due to some optimization problems. In this work, we propose an optimization method that is adapted to the MI cost function and gives a practical solution for augmented reality application. We show that by refining the computation of the Hessian matrix and using a specific optimization approach, the tracking results are far more robust and accurate than the existing solutions. A new approach is also proposed to speed up the computation of the derivatives and keep the same optimization efficiency. To validate the advantages of the proposed approach, several experiments are performed. The ESM and the proposed MI tracking approaches are compared on a standard dataset. We also show the robustness of the proposed approach on registration applications with different sensor modalities: map versus satellite images and satellite images versus airborne infrared images within different AR applications.

Abstract. Existing monocular vision-based SLAM systems favour interest point features as landmarks, but these are easily occluded and can only be reliably matched over a narrow range of viewpoints. Line segments offer an interesting alternative, as line matching is more stable with respect to viewpoint changes and lines are robust to partial occlusion. In this paper we present a model-based SLAM system that uses 3D line segments as landmarks. Unscented Kalman filters are used to initialise new line segments and generate a 3D wireframe model of the scene that can be tracked with a robust model-based tracking algorithm. Uncertainties in the camera position are fed into the initialisation of new model edges. Results show the system operating in real-time with resilience to partial occlusion. The maps of line segments generated during the SLAM process are physically meaningful and their structure is measured against the true 3D structure of the scene. 1

Abstract not found

The goal of this paper is to present an overview of robust estimation techniques with a special focus on robotic vision applications. In this particular context, constraints due computation time have to be considered in the choice of the estimation algorithm. Among the numerous techniques proposed in the literature to obtained robust estimation we have, not being exhaustive, Hough transform, RANSAC (Random Sample Consensus), the LMedS (Least Median of Squares), the Mestimators, etc. In this overview, we describe these various approaches in the light of a simple example. Finally, we illustrate the use of robust estimation techniques by various examples in real-time robot vision.

Abstract — For a space rendezvous mission, autonomy imposes stringent performance requirements regarding navigation. For the final phase, a vision-based navigation can be a solution. A 3D model-based tracking algorithm has been studied and tested on a mock-up of a telecommunication satellite, using a 6-DOF robotic arm, with satisfactory results, in terms of precision of the pose estimation and computational costs. Quantitative tests in open loop have been carried out to show the robustness of the algorithm to relative inter frame motions chaser/target, orientation variations and illumination conditions. The tracking algorithm has also been successfully implemented in a closed loop chain for visual servoing. I.

In this paper we introduce a real-time human arm motion detector that has been developed to aid the home-based rehabilitation of stroke patients. Two tri-axial inertial sensors are adopted to measure the orientation of the arm. Kinematics models then allow us to recover the coordinates of the wrist and elbow joints, given a still shoulder joint. One of the significant contributions of this paper is the use of a total variation based optimization in smoothing the erroneous measurements due to rapid or unstable movements. Comprehensive experiments demonstrate favorable performance of the proposed inertial tracking system in different sensor positions and motion speeds, compared to the outcomes of a marker-based optical motion tracker that is commercially available.

This paper proposes a real-time, robust and efficient 3D model-based tracking algorithm for visual servoing. A virtual visual servoing approach is used for 3D tracking. This method is similar to more classical non-linear pose computation techniques. Robustness is obtained by integrating an M-estimator into the virtual visual control law via an iteratively reweighted least squares implementation. The presented approach is also extended to the use of multiple cameras. Results show the method to be robust to occlusion, changes in illumination and miss-tracking. inria-00350286, version 1- 6 Jan 2009 1

Robotic microassembly is a promising way to fabricate micrometric components based three dimensions (3D) compound products where the materials or the technologies are incompatible: structures, devices, Micro Electro Mechanical Systems (MEMS), Micro Opto Electro Mechanical Systems

This paper presents a method for camera pose tracking that uses a partial knowledge about the scene. The method is based on monocular vision Simultaneous Localization And Mapping (SLAM). With respect to classical SLAM implementations, this approach uses previously known information about the environment (rough map of the walls) and profits from the various available databases and blueprints to constraint the problem. This method considers that the tracked image patches belong to known planes (with some uncertainty in their localization) and that SLAM map can be represented by associations of cameras and planes. In this paper, we propose an adapted SLAM implementation and detail the considered models. We show that this method gives good results for a real sequence with complex motion for augmented reality (AR) application. 1