Gradient-based optimization is a very efficient strategy to solve the direct visual tracking (DVT) problem using transformation models with many degrees of freedom (DOF). Even though popular DVT methods use the sum of squared differences as similarity function, this approach is not robust to illumination variations often verified in practice. One technique to compensate illumination variations is through an illumination model, which, in turn, increases the total number of parameters to be computed. High quality augmented reality and robotic systems demand fast tracking speeds, which can be impaired by the computational complexity added by the illumination model. In this paper, we propose a robust DVT method capable of tracking in extreme illumination conditions. Building upon the sum of conditional variance as similarity function, we propose a novel tracking method that significantly reduces the computational effort compared to similar methods proposed in the literature. We provide extensive experiments and quantitative analysis using challenging videos to attest the advantages of the proposed method.

This paper concerns the problem of position and attitude estimation based on the fusion of complementary sensors. Nonlinear observers are proposed in order to obtain high-quality and high-rate estimates from raw position/attitude data, and measurements obtained from an inertial measurement unit. Estimates are improved through the online identification of diverse measurement biases, and a novel method for con current position estimation and accelerometer bias estimation is presented. Almost global asymptotic convergence of the pose observer is proved based on a Lyapunov-like approach, and several implementation issues are addressed. Comparison with classical existing methods illustrate the relevance of the proposed approach.

This paper concerns the problem of pose estimation for an inertial-visual sensor. It is well known that IMU bias, and calibration errors between camera and IMU frames can impair the achievement of high-quality estimates through the fusion of visual and inertial data. The main contribution of this work is the design of new observers to estimate pose, IMU bias and camera-to-IMU rotation. The observers design relies on an extension of the so-called passive complementary filter on SO(3). Stability of the observers is established using Lyapunov functions under adequate observability conditions. Experimental results are presented to assess this approach.

This paper presents a new application in the field of rehabilitation robotics. It is part of a research project which consists of the development of a robotic leg prosthesis for above knee amputees. This application aims at providing real-time estimates of the prosthesis foot orientation with respect to ground. An Extended Kalman Filter (EKF) is used in order to estimate the foot orientation based on gyroscope and infrared measurements. Moreover a constraint equation is also included in the EKF to guarantee the constraints in the estimates. Preliminary tests were performed in a platform built for the prosthesis prototype. The results discussed in this paper reveal the feasibility of such technique.