We present a general theory of topological maps whereby sensory input, topological and local metrical information are combined to define the topological maps explaining such information. Topological maps correspond to the minimal models of an axiomatic theory describing the relationships between the different sources of information explained by a map. We use a circumscriptive theory to specify the minimal models associated with this representation.

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: 1. Odometry; 2. Inertial Navigation; 3. Magnetic Compasses; 4. Active Beacons; 5. Global Positioning Systems; 6. Landmark Navigation; and 7. Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field

For mobile robots to be successful, they have to navigate safely in populated and dynamic environments. While recent research has led to a variety of localization methods that can track robots well in static environments, we still lack methods that can robustly localize mobile robots in dynamic environments, in which people block the robot's sensors for extensive periods of time or the position of furniture may change. This paper proposes extensions to Markov localization algorithms enabling them to localize mobile robots even in densely populated environments. Two different filters for determining the "believability" of sensor readings are employed. These filters are designed to detect sensor readings that are corrupted by humans or unexpected changes in the environment. The technique was recently implemented and applied as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Extensive empirical tests involving datasets re...

In recent years, particle filters have solved several hard perceptual problems in robotics. Early successes of particle filters were limited to low-dimensional estimation problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The fact that every model---no mater how detailed---fails to capture the full complexity of even the most simple robotic environments has lead to specific tricks and techniques essential for the success of particle filters in robotic domains. This article surveys some of these recent innovations, and provides pointers to in-depth articles on the use of particle filters in robotics.

This paper presents methods of localization using cooperating landmarks (beacons) that provide the ability to measure range only. Recent advances in radio frequency technology make it possible to measure range between inexpensive beacons and a transponder. Such a method has tremendous benefit since line of sight is not required between the beacons and the transponder, and because the data association problem can be completely avoided. If the positions of the beacons are known, measurements from multiple beacons can be combined using probability grids to provide an accurate estimate of robot location. This estimate can be improved by using Monte Carlo techniques and Kalman filters to incorporate odometry data. Similar methods can be used to solve the simultaneous localization and mapping problem (SLAM) when beacon locations are uncertain. Experimental results are presented for robot localization. Tracking and SLAM algorithms are demonstrated in simulation. 1

dedeoglu, gaurav @ robotics.usc.edu Abstract. This paper describes a landmark-based algorithm for map matching, and demonstrates its use in e ciently combining topological maps of indoor environments built by autonomous mobile robots. Results of the rst implementation are presented, in which two robots with di erent mechanics and sensory capabilities independently explore their environments with no a priori maps. Each robot is initially unaware of the other's relative position and orientation. Using the match algorithm, they eventually merge their maps into a topologically correct single map in real time, based only on the feature sets they have discovered independently. 1

We present a behavior-based technique for incremental on-line mapping and autonomous navigation for mobile robots, specifically geared for time-critical indoor exploration tasks. The experimental platform used is a Pioneer AT mobile robot endowed with seven sonar transducers, a drift-stabilized gyro, a compass and a pan-tilt color camera. While the thrust of our work is the autonomous generation of real-time topological maps of the environment, both metric and topological descriptions of the environment are created in real time, each preserving its unique representational power and ease-of-use. We also present initial results on multi-robot cooperative topological mapping. The building blocks of the topological map are corridors, junctions and open/closed doors, augmented with absolute heading and metric information. Since the robot does not begin with an a priori map, all environmental features have to be evaluated at run-time to ensure safe navigation and efficient exploration. Our enhanced deadreckoning algorithm is backed up by the cyclic nature of indoor environments that provides additional hints for self-localization corrections. In addition, domain knowledge (such as perpendicular hallways) is used to actively correct maps as they are built on-line. All navigation, exploration, map building and self-localization capabilities are implemented as tightly-coupled behaviors, run by the onboard CPU. robots 1.

Much attention has been given to CONDENSATION methods for mobile robot localization. This has resulted in somewhat of a breakthrough in representing urncertainty for mobile robots. In this paper we use CONDENSATION with planned sampling as a tool for doing feature based global localization in a large and semi-structured environment. This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner. We show eperimental results that highlight the information content of the different features, and point to fruitful combinations. Accuracy, computation time and the ability to narrow down the search space are among the measures used to compare the features. From the comparison of the features, some general guidelines are drawn for determining good feature types.

Service robotics is currently a pivotal research area in robotics, with enormous societal potential. Since service robots directly interact with people, nding \natural" and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such asnavigation and manipulation, relatively few robotic systems are equipped with exible user interfaces that permit controlling the robot by \natural " means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through o ce environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures de ned through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to speci c locations that need to be cleaned and instructs the robot to pick up trash. 1.

Abstract. In this paper we present our advances in a data structure, the Gap Navigation Tree (GNT), useful for solving different visibility-based robotic tasks in unknown planar environments. We present its use for optimal robot navigation in simply-connected environments, locally optimal navigation in multiply-connected environments, pursuit-evasion, and robot localization. The guiding philosophy of this work is to avoid traditional problems such as complete map building and exact localization by constructing a minimal representation based entirely on critical events in online sensor measurements made by the robot. The data structure is introduced from an information space perspective, in which the information used among the different visibility-based tasks is essentially the same, and it is up to the robot strategy to use it accordingly for the completion of the particular task. This is done through a simple sensor abstraction that reports the discontinuities in depth information of the environment from the robot’s perspective (gaps), and without any kind of geometric measurements. The GNT framework was successfully implemented on a real robot platform. 1