Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions

This paper introduces a method for the substantial reduction of heading errors in inertial navigation systems used under GPS-denied conditions. Presumably, the method is applicable for both vehicle-based and personal navigation systems, but experiments were performed only with a personal navigation system called 'personal dead reckoning' (PDR). In order to work under GPS-denied conditions, the PDR system uses a foot-mounted inertial measurement unit (IMU). However, gyro drift in this IMU can cause large heading errors after just a few minutes of walking. To reduce these errors, the map-matched heuristic drift elimination (MAPHDE) method was developed, which estimates gyro drift errors by comparing IMU-derived heading to the direction of the nearest street segment in a database of street maps. A heuristic component in this method provides tolerance to short deviations from walking along the street, such as when crossing streets or intersections. MAPHDE keeps heading errors almost at zero, and, as a result, position errors are dramatically reduced. In this paper, MAPHDE was used in a variety of outdoor walks, without any use of GPS. This paper explains the MAPHDE method in detail and presents experimental results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90785/1/0957-0233_22_2_025205.pd

Real-time Map-building for Fast Mobile Robot Obstacle Avoidance

ABSTRACT This paper introduces HIMM (histogramic in-motion mapping), a new method for real-time map building with a mobile robot in motion. HIMM represents data in a two-dimensional array (called a histogram gjid) that is updated through rapid continuous sampling of the onboard range sensors during motion. Rapid in-motion sampling results in a statistical map representation that is well-suited to modeling inaccurate and noisy range-sensor data. HIMM is integral part of an obstacle avoidance algorithm and allows the robot to immediately use the mapped information in real-time obstacleavoidance. The benefits of this integrated approach are twofold: (1) quick, accurate mapping; and (2) safe navigation of the robot toward a given target. HIMM has been implemented and tested on a mobile robot. Its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created, while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0.78m/sec

Mobile robot positioning: Sensors and techniques

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search of a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This article provides a review of relevant mobile robot positioning technologies. The article 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 of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this article serve only to represent their respective categories, but they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevily, not all could be cited in this article. © 1997 John Wiley & Sons, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34938/1/2_ftp.pd

Measurement and Correction of Systematic Odometry Errors in Mobile Robots

Odometry is the most widely used method for determining the momentary position of a mobile robot. In most practical applications odometry provides easily accessible real-time positioning information in-between periodic absolute position measurements. The frequency at which the (usually costly and/or time-consuming) absolute measurements must be performed depends to a large degree on the accuracy of the odometry system. This paper introduces practical methods for measuring and reducing odometry errors that are caused by the two dominant error sources in differential-drive mobile robots: (a) uncertainty about the effective wheelbase and (b) unequal wheel diameters. These errors stay almost constant over prolonged periods of time. Performing an occasional calibration as proposed here will increase the robot's odometric accuracy and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots, usually in a time-consuming and non-systematic trial and error approach. By contrast, the method described in this paper is systematic, provides near-optimal results, and it can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with our method. Some parts of the material in this paper were presented at the 1995 International Conference on Intelligent Robots and Systems (IROS '95), Pittsburgh, Pennsylvania, August 5-9, 1995; some other parts were presented at the 1995 SPIE Conference on Mobile Robots, Philadelphia, October 22-26, 1995. 2 1