For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that must all be solved in an integrated manner to promote survival. This point is illustrated here, within the framework of a review of localization strategies in mobile robots. The allothetic and idiothetic sensors that may be used by these robots to build internal representations of their environment, and the maps in which these representations may be instantiated, are first described. Then map-based navigation systems are categorized according to a 3-level hierarchy of localization strategies, which respectively call upon direct position inference, single-hypothesis tracking, and multiple-hypothesis tracking. The advantages and drawbacks of these strategies, notably with respect to the limitations of the sensors on which they rely, are discussed throughout the text.

This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m × 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods.

A new practical, high-performance mobile robot localization technique is described which is motivated by the fact that many man-made environments contain substantially flat, visually textured surfaces of persistent appearance.

A way to significantly advance robotic science is to perform experiments that can be replicated by other researchers and be used to compare different methods. This happens rarely in current robotics research. In this paper we present a methodology for performing experimental activities in the area of robotic mapping. The proposed methodology prescribes a number of issues that should be addressed when experimentally validating a mapping method. We present the application of the proposed methodology to a mapping system we have developed.

Studies of searching behaviour in bees by Cartwright and Collett led to a computational model of short-range insect guidance that has been successfully implemented on real robots.

We present an algorithm for merging two partial maps obtained with a laser range scanner into a single map.

Theoretical solutions based on the matching of 2-D range measurements with a map of the environment have been proposed to solve the robot localization problem. However, most of them have not been experimented in real conditions: the robot was stopped or it moved slowly during range data acquisition, and the environment was supposed to be static. In this paper, we propose and evaluate a dynamic localization method based on feature matching. Experiments carried out in real cluttered indoor environments including people and unknown obstacles show the good performance of the proposed algorithm against the classical solution based on Kalman filtering. 1

This paper presents our approach for laser-based local position tracking based on the data explored in a three-dimensional environmental model of an indoor environment. This algorithm is used to substitute the dead reckoning on a mobile robot to allow robust map generation and position dependent task triggering. The underlying concept of the local environmental model used for filtering of the sensor information allows an easy fusion of di#erent sources of the available information, like: a-priori knowledge, explored information and even fusion of the information from other sensor systems. This system is implemented and tested on our mobile robot.

Abstract This paper proposes an approach for motion planning in indoor environments based on incomplete and uncertain information from a line-based binocular stereo system. The primary goal of the planning process is to plan an optimal path through an unknown or partially known environment, depending on the information gained from exploration and the current mission goal. This paper presents an adaptable motion planner that supports sensor-based map construction, object recognition and navigation in an unknown environment while carrying out a mission. Also presented are some preliminary experimental results that demonstrate the utility of the approach. 1 Introduction A mobile robot needs knowledge about the environment to plan its actions and to fulfill the mission goals, which are often specified relative to the known obstacles in the local area. The environmental models can be known beforehand, but gradual changes in the environment deteriorate the usability of those models for sensor data interpretation. A better approach is to explore the environment and maintain the geometric models by using the sensor system. The idea for the planning system is to consider the global mission goal and hints from the sensor systems and interpretation modules to achieve the best information gain in a given period of time. Time constraints are also considered to optimize the planned path.

A new practical, high-performance mobile robot localization technique is described that is motivated by the fact that many man-made environments contain substantially flat, visually textured surfaces of persistent appearance. While the tracking of image regions is much studied in computer vision, appearance is still a largely unexploited localization resource in commercially relevant applications. We show how prior appearance models can be used to enable highly repeatable mobile robot guidance that, unlike commercial alternatives, is both infrastructure-free and free-ranging. Very large-scale mosaics are constructed and used to localize a mobile robot operating in the modeled environment. Straightforward techniques from vision-based localization and mosaicking are used to produce a field-relevant AGV guidance system based only on vision and odometry. The feasibility, design, implementation, and precommercial field qualification of such a guidance system are described. KEY WORDS—mobile robots, localization, mosaicking, visual tracking, visual odometry, image-based rendering, image