Simultaneous Localization and Map Building (SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter (EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the rec...

We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the problem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF met...

A dynamic feedback system is developed for estimating the headway and velocity in a longitudinal three-vehicle platoon. The estimation system is modeled using a particle filter (PF) and an unscented Kalman filter (UKF) that estimate them by measuring the acceleration rate and/or velocity of probe vehicle(s) in the platoon. State equations are defined as a discrete conservation equation of headw...

Recently, the Spherical Motion Models (SMM’s) have been introduced [1]. These new models have been developed for 3D local landmark-base Autonomous Navigation (AN). This paper is revealing new arguments and experimental results to support the SMM’s characteristics. The accuracy and the robustness in performing a specific task are the main concerns of the new investigations. To analyze their perf...

Equation (1) is called the measurement equation. It relates the measured observable variables that provide information on αt. We use Zt ∈ M (pt ×m) to denote the matrix of factor loadings. The Ht ∈M (pt × pt) matrix is the variance-covariance matrix of the measurement noise vector, εt. Equation (2) is called the transition equation. We use Gt ∈ M (m×m) to denote the matrix of factor coefficient...

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications in...

A method is proposed for determining dynamical and observational noise parameters in state and parameter identification from time series using Kalman filters. The noise covariances are estimated in a secondary optimization by maximizing the predictive likelihood of the data. The approach is based on internal consistency; for the correct noise parameters, the uncertainty projected by the Kalman ...

This paper presents a practical technique for modelbased 3D hand tracking. An anatomically accurate hand model is built from truncated quadrics. This allows for the generation of 2D profiles of the model using elegant tools from projective geometry, and for an efficient method to handle self-occlusion. The pose of the hand model is estimated with an Unscented Kalman filter (UKF), which minimize...