This article describes a methodology for programming robots known as probabilistic
robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

This paper describes the simultaneous localization and mapping (SLAM) problem and the
essential methods for solving the SLAM problem and summarizes key implementations and …

In this article, we present an overview of methods for sequential simulation from posterior
distributions. These methods are of particular interest in Bayesian filtering for discrete time …

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo
method with emphasis on probabilistic machine learning. Second, it reviews the main …

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective
means to solve the simultaneous localization and mapping problem. This approach uses a …

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

This article analyses the recently suggested particle approach to filtering time series. We
suggest that the algorithm is not robust to outliers for two reasons: The design of the …

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus,
reliable position estimation is a key problem in mobile robotics. We believe that probabilistic …