This paper provides a new fully-decentralized algorithm for Collaborative Localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies – which is particularly difficult in situations where sustained synchronous communication between robots cannot be guaranteed. Current approaches suffer from the need for p...

This brief technical note elaborates three well-known state estimators, which are used extensively in practice. These are the rather old-fashioned extended Kalman filter (EKF) and the recently-designed cubature Kalman filtering (CKF) and un-scented Kalman filtering (UKF) algorithms. Nowadays, it is commonly accepted that the contemporary techniques outperform always the traditional EKF in the a...

In recent works, the first author has developed a constraint estimation technique that can be applied to the kinematic tracking problem. This a posteriori technique maintains an unconstrained estimate based solely on the measurements while the constrained estimate is computed, if necessary, at each time step. This technique has been expanded to incorporate nonlinear systems and time-varying sys...

In this paper, we present an algorithm for autonomous stair climbing with a tracked vehicle. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicle’s interaction with the stair surface, or lighting conditions. Our approach relies on fast and accurate estimation of the robot’s heading and it...

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In general, the information of mass properties is required to control a spacecraft. The mass properties, mass and inertia, are changed by some activities, e.g., consumption of propellant, deployment of solar panel, sloshing, etc. Various estimation methods have been studied to obtain the accurate mass properties. The gyro-based attitude data including noise and bias needs to be compensated for ...

This paper presents methods for tracking moving objects in an outdoor environment. A robust tracking is achieved using feature fusion and multiple cameras. The proposed method integrates spatial position, shape and color information to track object blobs. The trajectories obtained from individual cameras are incorporated by an extended Kalman filter (EKF) to resolve object occlusion. Our result...

Estimating the structure of the human face is a long studied and difficult task. In this paper we present a new method for estimating facial structure from only a minimal number of salient feature points across a video sequence. The presented method uses both an Extended Kalman Filter (EKF) and a Kalman Filter (KF) to regress 3D Morphable Model (3DMM) shape parameters and solve 3D pose using a ...

We describe a model-based object tracking system that updates the connguration parameters of an object model based upon information gathered from a sequence of monocular images. Realistic object and imaging models are used to determine the expected visibility of object features, and to determine the expected appearance of all visible features. We formulate the tracking problem as one of paramet...

Abstract: This paper describes an automatic calibration procedure adopted to improve the localization of an outdoor mobile robot. The proposed algorithm estimates, by using an extended Kalman filter, the main kinematic parameters of the vehicles, such as the wheel radii and the wheelbase as well as the magnetometer offset. Several trials have been performed to validate the proposed strategy on ...