Recently, many authors have considered the problem of simultaneous localisation and mapping (SLAM). The paper addresses a somewhat different problem, that of cooperative localisation and mapping (CLAM). Basically, the CLAM approach involves two or more robots cooperating to build a map of the environment. This cooperation is not aimed at simply increasing the speed with which the map is constructed; rather, it is aimed at increasing the accuracy of the resultant maps. This paper describes some early work aimed at validating the CLAM concept. 1 Introduction Recently, many authors have considered the problem of simultaneous localisation and mapping (SLAM). The paper addresses a somewhat different problem, that of cooperative localisation and mapping (CLAM). The aim of SLAM, as opposed to CLAM, is to build a map of an unknown environment and simultaneously localise the robot with respect to this map. The map might be relational, or it might be defined with respect to some coordinate sy...

Recently, many authors have considered the problem of simultaneous localisation and mapping (SLAM). The paper addresses a somewhat different problem, that of cooperative localisation and mapping (CLAM). Basically, CLAM involves two or more robots cooperating to build a map of the environment. This cooperation is not aimed at simply increasing the speed with which the map is constructed; rather, it is aimed at increasing the accuracy of the resultant maps. This paper describes some early work aimed at validating the CLAM concept.

One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robot’s field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360º view of the surrounding environment as part of map construction. 1

One of the key difficulties facing any mobile robot is obtaining accurate, relevant and timely maps of the environment it inhabits. A great many sensing modalities have been applied to this task, including vision-based systems such as geometric modelling, steropsis and structured lighting, and non-vision systems such as sonar, radar and lidar. Unfortunately, vision-based systems have generally been distinguished by low speed and enormous computational requirements, whilst non-vision systems have been distinguished by either poor resolution (sonar) or high cost (radar and lidar). In response to these limitations, we have developed a vision-based mapping system that is fast, inexpensive, and requires only modest computational resources. The system comes in two parts: a visual range subsystem and a map building subsystem. The visual range subsystem acts as a kind of `virtual range sensor', which takes as input a stream of images and produces as output a stream of range-and-bearing measurements. The map 1 of 2 11/14/00 9:01 PM Fast Visual Mapping

Abstract—Mapping algorithms commonly use “radial sweeps ” of the surrounding environment as input. Producing a sweep is a challenging task for a robot using only vision. With no odometers to measure turn angles, a vision-based robot must have another method to verify rotations. In this paper we propose using the Radial Obstacle Profile (ROP) which gives the radial distance to the nearest obstacle in any direction in the robot’s field of view. By matching the ROPs before and after a turn, the robot should be able to verify that the expected angle of rotation matches the actual angle. Combining successive ROPs then produces a radial sweep. Keywords—computer and robot vision;wheeled mobile robots; mapping; radial obstacle profile I.

Recently, many authors have considered the problem of simultaneous localisation and mapping (SLAM). The paper addresses a somewhat different problem, that of cooperative localisation and mapping (CLAM). Basically, CLAM involves two or more robots cooperating to build a map of the environment. This cooperation is not aimed at simply increasing the speed with which the map is constructed; rather, it is aimed at increasing the accuracy of the resultant maps. This paper describes some early work aimed at validating the CLAM concept. Authors: Andrew Howard Department of Computer Science and Software Engineering The University of Melbourne andrbh@cs.mu.oz.au http://www.cs.mu.oz.au/~andrbh Les Kitchen Department of Computer Science and Software Engineering The University of Melbourne ljk@cs.mu.oz.au http://www.cs.mu.oz.au/~ljk 1 Cooperative Localisation and Mapping: Preliminary Report Andrew Howard and Les Kitchen Department of Computer Science and Software Engineering ...

MYNORCA is a vision-based navigation system for mobile robots, designed principally for operation in indoor environments. The system uses vision for detecting obstacles and locating natural landmarks. In addition, it is able to solve navigation problems in which the robot's initial location is completely unknown. In this paper, we present an overview of MYNORCA, describe its implementation and present some experimental results.