Presents results for state estimation based on noisy observations suffering from two different types of uncertainties. The first uncertainty is a stochastic process with given statistics. The second uncertainty is only known to be bounded, the exact underlying statistics are unknown. State estimation tasks of this kind typically arise in target localization, navigation, and sensor data fusion. A new estimator has been developed, that combines set theoretic and stochastic estimation in a rigorous manner. The estimator is efficient and, hence, well-suited for practical applications. It provides a continuous transition between the two classical estimation, concepts, because it converges to a set theoretic estimator, when the stochastic error goes to zero, and to a Kalman filter, when the bounded error vanishes. In the mixed noise case, the new estimator provides solution sets that are uncertain in a statistical sense.

This paper presents a new approach for estimating the state of a linear dynamic system when two different types of uncertainties are present simultaneously. The first type of uncertainty is a stochastic process with given distribution. The second type of uncertainty is only known to be bounded, the exact underlying distribution is unknown. This includes inequality constraints between state variables, geometric tolerances, and bounded noise sources which are possibly correlated. For this generalized uncertainty model, a new recursive estimator has been developed. The new estimator unifies Kalman filtering and set theoretic filtering. It converges to a Kalman filter, when the bounded uncertainty goes to zero, and it converges to a set theoretic filter, when the stochastic noise vanishes. In the case of mixed uncertainties, the new estimator provides solution sets that are uncertain in a statistical sense.

We present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an extended Kalman filter integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MACROBE. We have chosen two types of complementary sensory information: (1) range images; (2) intensity images; obtained from a laser sensor. Results of these experiments show that fusing simple and computationally inexpensive sensory information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.

Considers state estimation based on observations which are simultaneously corrupted by a deterministic amplitude-bounded unknown bias and a possibly unbounded random process. This problem is solved by developing a combined set theoretic and Bayesian recursive estimator. It provides a continuous transition between both concepts in that it converges to a set theoretic estimator when the stochastic error vanishes and to a Bayesian estimator when the deterministic error vanishes. In the mixed noise case, the new estimator supplies solution sets defined by bounds that are uncertain in a statistical sense.

Presents the 2D version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an EKF integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. The authors apply the SPmodel to the problem of location estimation in mobile robotics, experimenting with the mobile robot MACROBE. The authors have chosen two types of sensorial information whose complementary nature allows a mobile robot to localize itself precisely in a known environment: range images and intensity images obtained from a laser sensor. By measuring distance to objects, range basically provides position information. On the other hand, the intensity image provided by the laser sensor (equivalent to a monocular vision system) can be used to detect vertical edges, corresponding to corners, frames, etc., which mainly provide angular information. Results of these experiments show that fusing simple and computationally inexpensive sensorial information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.

This article describes the localization system of a free-navigating mobile robot. Absolute position and orientation of the vehicle are determined by matching vertical planar surfaces extracted from a 3D-laser-range-image with corresponding surfaces predicted from a 3D-environmental model. Continuous localization is achieved by fusing single-image localization and dead-reckoning data by means of a statistical uncertainty evolution technique. Extensive closed-loop experiments with the full-scale mobile robot MAC-ROBE proved robustness, accuracy and real-time capability of this localization scheme.