A Constant-Time Algorithm for Vector Field SLAM using an Exactly Sparse Extended Information Filter

Abstract — Designing a localization system for a low-cost robotic consumer product poses a major challenge. In previous work, we introduced Vector Field SLAM [5], a system for simultaneously estimating robot pose and a vector field induced by stationary signal sources present in the environment. In this paper we show how this method can be realized on a low-cost embedded processing unit by applying the concepts of the Exactly Sparse Extended Information Filter [15]. By restricting the set of active features to the 4 nodes of the current cell, the size of the map becomes linear in the area explored by the robot while the time for updating the state can be held constant under certain approximations. We report results from running our method on an ARM 7 embedded board with 64 kByte RAM controlling a Roomba 510 vacuum cleaner in a standard test environment. NS spot1 X (sensor units) Spot1 X readings Node X1 estimate

AMOS: Comparison of scan matching approaches for selflocalization in indoor environments

This paper describes results from evaluating different self-localization approaches in indoor environments for mobile robots. The examined algorithms are based on 2d laser scans and an odometry position estimate and do not need any modifications in the environment. Due to the goals of our project an important requirement for self-localization is the ability to cope with office-like environments as well as with environments without orthogonal and rectilinear walls. Furthermore, the approaches have to be robust enough to cope with slight modifications in the daily environment and should be fast enough to be used on-line on board of the robot system. To fulfil these requirements we made some extensions to existing approaches and combined them in a suitable manner. Real world experiments with our robot within the everyday environment of our institute show that the position error can be kept small enough to perform navigation tasks. Keywords: Mobile Robot, Self-Localization 1

Fast, Accurate, and Robust Self-Localization in the RoboCup Environment

. Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for RoboCup'98, it turned out that all existing approaches did not meet our requirements of being fast, accurate, and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. We additionally present experimental evidence that our method outperforms other methods in the RoboCup environment. 1 Introduction Robotic soccer is an interesting scientific challenge [11] and an ideal domain for testing new ideas and demonstrating existing techniques. One of our main intentions in participating in last year's RoboCup'98 [1] was to demonstrate the usefulness of self-localization techniques that we have developed [9]. It turned out, however, that all existing self-localization techniques were not efficient enough for a dynamic environment..