Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.

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...

This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research problems.

Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process.

This paper presents a high quality, low cost 3D laser range finder designed for autonomous mobile systems. The 3D laser is built on the base of a 2D range finder by the extension with a standard servo. The servo is controlled by a computer running RT-Linux. The scan resolution ( 5 cm) for a complete 3D scan of an area of 150 (h) 90 (v) degree is up to 115000 points and can be grabbed in 12 seconds. Standard resolutions e.g. 150 (h) 90 (v) degree with 22500 points are grabbed in 4 seconds. While scanning, different online algorithms for line and surface detection are applied to the data. Object segmentation and detection are done offline after the scan. The implemented software modules detect overhanging objects blocking the path of the robot. With the proposed approach a cheap, precise, reliable and real-time capable 3D sensor for autonomous mobile robots is available and the robot navigation and recognition in real-time is improved. 1.

We describe Localization-Space Trails (LOST), a method that enables a team of robots to navigate between places of interest in an initially unknown environment using a trail of landmarks. The landmarks are not physical; they are waypoint coordinates generated on-line by each robot and shared with team-mates. Waypoints are specified in each robot's local coordinate system, and contain references to features in the world that are relevant to the team's task and common to all robots. Using these task-level references, robots can share waypoints without maintaining a global coordinate system.

With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of autonomously moving objects. The state estimators of different robots cooperate to increase the accuracy and reliability of the estimation process. This cooperation between the robots enables them to track temporarily occluded objects and to faster recover their position after they have lost track of it. The method is empirically validated based on experiments with a team of physical robots.

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.

A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment. Sensor data is assumed to be provided in the form of a Gaussian distribution over the space of robot poses. A tree of hypotheses is built, representing the possible data association histories for the system. Covariance intersection is used for the fusion of the Gaussians whenever a data association decision is taken. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probabilities. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.

For mobile robots, accurate localization is a key issue. Cooperation between multiple robots can, among other benefits, improve their localization capabilities. This thesis investigates the assumption that it is possible to optimize the trajectories of cooperating robots with respect to their localization performance. We present an approach to optimizing entire trajectories for a group of mobile robots that use one another as localization beacons. The cost function we seek to optimize is a measure of localization uncertainty (as opposed to common criteria such as distance travelled or time). Following an overview of previous work on localization of mobile robots, we present our models in an estimation-theoretic framework for cooperative localization. They serve to analyze geometric sensing configurations and to set up the optimization problem. We then apply a Sequential Quadratic Programming method to compute optimized trajectories for selected scenarios. Our findings show that the resulting optimized trajectories yield considerably higher