Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a ne-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a ltering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described he...

The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectory-following and hill-climbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model ...

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Abstract. In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle’s motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer. Keywords: laser scanning, navigation, self-localization, mobile robots, 3D modeling, Monte-Carlo localization 1.

| We propose and investigate a general framework for hierarchical modeling of partially observable environments, such as oce buildings, using Hierarchical Hidden Markov Models (HHMMs). Our main goal is to explore hierarchical modeling as a basis for designing more ecient methods for model construction and useage. As a case study we focus on indoor robot navigation and show how this framework can be used to learn a hierarchy of models of the environment at dierent levels of spatial abstraction. We introduce the idea of model reuse that can be used to combine already learned models into a larger model. We describe an extension of the HHMM model to includes actions, which we call hierarchical POMDPs, and describe a modied hierarchical Baum-Welch algorithm to learn these models. We train dierent families of hierarchical models for a simulated and a real world corridor environment and compare them with the standard \at" representation of the same environment. We show that the hierarchical POMDP approach, combined with model reuse, allows learning hierarchical models that t the data better and train faster than at models.

Abstract — This paper proposes a hybrid approach to the problem of collision avoidance for indoor mobile robots. The DWA (short for: model-based dynamic window approach) integrates sensor data from various sensors with information extracted from a map of the environment, to generate collision-free motion. A novel integration rule ensures that with high likelihood, the robot avoids collisions with obstacles not detectable with its sensors, even if it is uncertain about its position. The approach was recently implemented and tested extensively as part of an installation, in which a mobile robot gave interactive tours to visitors of the “Deutsches Museum Bonn. ” Here our approach was essential for the success of the entire mission, because a large number of ill-shaped obstacles prohibited the use of purely sensor-based methods for collision avoidance. I.

In this paper we present a probabilistic approach for mobile robot localisation using an incomplete topological world model. The method uses multi{hypothesis Kalman lter based pose tracking combined with a probabilistic formulation of hypothesis correctness to on{line generate and track Gaussian pose hypotheses. Apart from a lower computational complexity, this has the advantage over traditional grid based methods that incomplete and topological world model information can be utilised. Furthermore, the method generates movement commands for the platform in order to optimise the information gathering for the pose estimation process. 1

Indoor environments can typically be divided into places with different functionalities like kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. This paper presents a supervised learning approach to label different locations using boosting. We train a classifier using features extracted from vision and laser range data. Furthermore, we apply a Hidden Markov Model to increase the robustness of the final classification. Our technique has been implemented and tested on real robots as well as in simulation. The experiments demonstrate that our approach can be utilized to robustly classify places into semantic categories. We also present an example of localization using semantic labeling.

. Two major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments, and the view of integration as a basic research issue. Where reasonable, we try to use the same representations to allow different components to work more readily together and to allow better and more natural integration of and communication between these components. In this paper, we describe our most recent work in integrating mobile robot exploration, localization, navigation, and planning through the use of a common representation, evidence grids. Keywords: mobile robots, localization, planning, navigation, exploration, evidence grids, integration 1. Introduction A central theme of our research is the view of integration as a basic research issue, studying the combination of different, complementary capabilities. One principle that allows integration is the use of unifying representations. Where reasonable, we try to use the same represent...

Behaviour coordination is a notorious problem in mobile robotics. Behaviours are either in competition or collaborating to achieve the goals of a system, which leads to requirements for arbitration and/or fusion of control signals. In most systems the arbitration is specified in terms of "events" that denote positions or sensory input. The detection of these events allows discrete switching between groups of behaviours. In contrast, the fusion of behaviours is often achieved using potential fields, fuzzy rules, or superposition. In most cases, the underlying theoretical foundation is rather weak and the behaviour switching results in discrete changes in the overall system dynamics. In this paper, we present a scheme for behaviour coordination that is grounded in the dynamical systems approach. The methodology provides a solid theoretical basis for analysis and design of individual behaviours and their coordination. This coordination framework is demonstrated in the context of a domestic robot for fetch-and-carry type tasks. It is here shown that behaviour coordination can be analyzed as an integral part of the design to facilitate smooth transition and fusion between behaviours.