We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy input, the algorithm estimates the robot's position and orientation with respect to the map of the environment. The algorithm makes efficient use of our representation of the landmarks by complex numbers. The algorithm runs in time linear in the number of landmarks. We present results of simulations and propose how to use our method for robot navigation.

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

This paper presents the key steps involved in the design, calibration and error modelling of a low cost odometry system capable of achieving high accuracy dead-reckoning. A consistent error model for estimating position and orientation errors has been developed. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark test [4]. Experimental results show that, despite its low cost, our system's performance, with regard to dead-reckoning accuracy, is comparable to some of the best reported odometry vehicle.

Evidence gridsprovide a uniform representation for fusing temporally and spatially distinct sensor readings. However, the use of evidence grids requires that the robot be localized within its environment. Odometry errors typically accumulate over time, making localization estimates degrade, and introducing signi-cant errors into evidence grids as they are built. We have addressed this problem by developing a method for \continuous localization", in which the robot corrects its localization estimates incrementally and on the y. Assuming the mobile robot has a map of its environment represented as an evidence grid, localization is achieved by building a series of \local perception grids " based on localized sensor readings and the current odometry, and then registering the local and global grids. The registration produces an o set which is used to correct the odometry. Results are given on the e ectiveness of this method, and quantify the improvement of continuous localization over dead reckoning. We also compare di erent techniques for matching evidence grids and for searching registration o sets. 1

This paper presents an approach for localization using geometric features from a 360 # laser range finder and a monocular vision system. Its practicability under conditions of continuous localization during motion in real time (referred to as on-the-fly localization) is investigated in large-scale experiments. The features are infinite horizontal lines for the laser and vertical lines for the camera. They are extracted using physically well-grounded models for all sensors and passed to a Kalman filter for fusion and position estimation. Positioning accuracy close to subcentimeter has been achieved with an environment model requiring 30 bytes/m 2 . Already with a moderate number of matched features, the vision information was found to further increase this precision, particularly in the orientation. The results were obtained with a fully self-contained system where extensive tests with anoverall length of more than 6.4 kmand 150,000 localization cycles have been conducted. The final testbed for this localization system was the Computer 2000 event, an annual computer tradeshow in Lausanne, Switzerland, where during 4 days visitors could give high-level navigation commands to the robot via a web interface. This gave us the opportunity to obtain results on long-term reliability and verify the practicability of the approach under application-like conditions. Furthermore, general aspects and limitations of multisensor on-the-fly localization are discussed. 2001 Elsevier Science B.V. All rights reserved. Keywords: Mobile robot localization; On-the-fly localization; Position tracking; Multisensor data fusion; Kalman filtering 1.

In this paper, we present the two-dimensional (2-D) version of the symmetries and perturbation model (SPmodel), a probabilistic representation model and an EKF integration mechanism for uncertain geometric information that is suitable for sensor fusion and integration in multisensor systems. We apply the SPmodel to the problem of location estimation in indoor mobile robotics, experimenting with the mobile robot MACROBE. We have chosen two types of complementary sensory information: 1) range images; 2) intensity images; obtained from a laser sensor. Results of these experiments show that fusing simple and computationally inexpensive sensory information can allow a mobile robot to precisely locate itself. They also demonstrate the generality of the proposed fusion and integration mechanism.

This paper concerns the application of techniques from estimation theory to the problem of navigation and perception for a mobile robot. After a brief introduction, a hierarchical architecture is presented for the design of a mobile robot navigation system. The control system for a mobile robot is found to decompose naturally into a hierarchy of control loops, where the levels are defined by the abstraction of the data and the cycle time of the feed-back control. The levels that occur naturally are identified as the level of signal, device, behaviour, and task. Estimation of the position of the vehicle with respect to the external world is fundamental to navigation. Modeling the contents of the immediate environment is equally fundamental. Estimation theory is provides a basic set of tools for position estimation and environmental modeling. These tools provide an elegant and formally sound method for combining internal and external sensor information from different sources, operating a...

. 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...

In this paper a new localization approach combining the metric and topological paradigm is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract lines for the metric localization and doors, discontinuities and hallways for the topological approach. The approach has been widely tested in a 50 x 25 m portion of the institute building with the new fully autonomous robot Donald Duck. 25 randomly generated test missions have been performed with a success ratio of 96 % and a mean error at the goal point of 9 mm for an overall trajectory length of 1.15 km. Future work will focus on a similar hybrid approach for simultaneous localization and automatic mapping. 1.

Abstract—This paper presents an error modeling of an odometry system for a synchronous-drive system and a possible strategy for evaluating this error. The odometry error is modeled by introducing four parameters characterizing its systematic and nonsystematic components (translational and rotational). The nonsystematic errors are expressed in terms of a covariance matrix, which depends on both the previous four parameters and the path followed by the mobile robot. In contrast to previous approaches which require assuming a particular path (straight or circular) in order to compute this covariance matrix, here general formulas are derived. We suggest a possible strategy for simultaneously estimating the four model parameters. As we will show, our strategy only requires measuring the change in the orientation and position between the initial and final configurations of the robot, related to suitable robot motions. In other words, it is unnecessary to know the actual path followed by the robot. We illustrate the proposed strategy by discussing the accuracy of the parameters estimation and by showing some experimental results obtained with the mobile robot Nomad150. Index Terms—Localization, odometry, robot navigation. I.