This CD-ROM is the electronic version of a comprehensive survey of the state of the art in sensors, systems, methods and technologies utilized by mobile robots to determine their position in the environment. The many potential "solutions" are roughly categorized into two groups: relative and absolute position measurements. The first includes odometry and inertial navigation; the second comprises active beacons, artificial and natural landmark recognition, and model matching. The authors compare and analyze these different methods based on technical publications and on commercial product and patent information. Comparison is centered around the following criteria: accuracy of position and orientation measurements, equipment needed, cost, sampling rate, effective range, computational power required, processing needs, and other special features. No robotics hobbyist or professional should be without this extraordinarily comprehensive look at robot positioning.
This CD-ROM also includes seven 2- to 4-minute video clips that can be played back on Macs, PCS, and possibly UNIX workstations. Also included are 17 background papers and detailed author information. The content of the CD-ROM includes a total of 441 MB of data (of which 36 MB are non-video information).
The CD-ROM is available for $39.95 (price subject to change without further notice) from publisher A. K. Peters, Ltd., Wellesley, MA
NOTE: As of 1999 this CD-ROM is "out of print". However, you can download the CD-ROM publication in its entirety (without the video clips and under the title "Where am I" Report).
(1) Dr. Johann Borenstein The University of Michigan Department of Mechanical Engineering and Applied Mechanics Mobile Robotics Laboratory 1101 Beal Avenue Ann Arbor, MI 48109 Ph.: (313) 763-1560 Fax: (313) 944-1113 Email: firstname.lastname@example.org
(2) Commander H. R. Everett Naval Command, Control, and Ocean Surveillance Center RDT&E Division 5303 271 Catalina Boulevard San Diego CA 92152-5001 Ph.:(619) 553-3672 Fax:(619) 553-6188 Email: Everett@NOSC.MIL
(3) Dr. Liqiang Feng The University of Michigan Department of Mechanical Engineering and Applied Mechanics Mobile Robotics Laboratory 1101 Beal Avenue Ann Arbor, MI 48109 Ph.: (313) 936-9362 Fax: (313) 763-1260 Email: Feng@engin.umich.edu
This research was sponsored by the Office of Technology Development, U.S. Department of Energy, under contract DE-FG02-86NE37969 with the University of Michigan.
The authors wish to thank the Department of Energy (DOE), and especially Dr. Linton W. Yarbrough, DOE Program Manager, Dr. William R. Hamel, D&D, Technical Coordinator, and Dr. Clyde Ward, Landfill Operations Technical Coordinator for their technical and financial support of the research, which forms the basis of this work.
Parts of the text were adapted from Sensors for Mobile Robots: Theory and Application, by H. R. Everett, A K Peters, Ltd., Wellesley, MA, Publishers.
Chapter 9 was contributed entirely by Sang W. Lee from the Artificial Intelligence Lab at the University of Michigan
Significant portions of Chapter 3 were adapted from "Global Positioning System Receiver Evaluation Results." by Raymond H. Byrne, originally published as Sandia Report SAND93-0827, Sandia National Laboratories, 1993.
The authors further wish to thank Professors David K. Wehe and Yoram Koren at the University of Michigan for their support, and Mr. Harry Alter (DOE) who has befriended many of the graduate students and sired several of our robots. Thanks are also due to Todd Ashley Everett for making most of the line-art drawings.
Leonard and Durrant-Whyte  summarized the general problem of mobile robot navigation by three questions: "Where am I?," "Where am I going?," and "How should I get there?." This book surveys the state-of-the-art in sensors, systems, methods, and technologies that aim at answering the first question, that is: robot positioning in its environment.
Perhaps the most important result from surveying the vast body of literature on mobile robot positioning is that to date there is no truly elegant solution for the problem. The many partial solutions can roughly be categorized into two groups: relative and absolute position measurements. Because of the lack of a single, generally good method, developers of automated guided vehicles (AGVs) and mobile robots usually combine two methods, one from each category. The two categories can be further divided into the following subgroups.
a. Odometry This method uses encoders to measure wheel rotation and/or steering orientation. Odometry has the advantage that it is totally self-contained, and it is always capable of providing the vehicle with an estimate of its position. The disadvantage of odometry is that the position error grows without bound unless an independent reference is used periodically to reduce the error [Cox, 1991].
b. Inertial Navigation This method uses gyroscopes and sometimes accelerometers to measure rate of rotation and acceleration. Measurements are integrated once (or twice) to yield position. Inertial navigation systems also have the advantage that they are self-contained. On the downside, inertial sensor data drifts with time because of the need to integrate rate data to yield position; any small constant error increases without bound after integration. Inertial sensors are thus unsuitable for accurate positioning over an extended period of time. Another problem with inertial navigation is the high equipment cost. For example, highly accurate gyros, used in airplanes, are inhibitively expensive. Very recently fiber-optic gyros (also called laser gyros), which are said to be very accurate, have fallen dramatically in price and have become a very attractive solution for mobile robot navigation.
c. Active Beacons This method computes the absolute position of the robot from measuring the direction of incidence of three or more actively transmitted beacons. The transmitters, usually using light or radio frequencies, must be located at known sites in the environment.
d. Artificial Landmark Recognition In this method distinctive artificial landmarks are placed at known locations in the environment. The advantage of artificial landmarks is that they can be designed for optimal detectability even under adverse environmental conditions. As with active beacons, three or more landmarks must be "in view" to allow position estimation. Landmark positioning has the advantage that the position errors are bounded, but detection of external landmarks and real-time position fixing may not always be possible. Unlike the usually point-shaped beacons, artificial landmarks may be defined as a set of features, e.g., a shape or an area. Additional information, for example distance, can be derived from measuring the geometric properties of the landmark, but this approach is computationally intensive and not very accurate.
e. Natural Landmark Recognition Here the landmarks are distinctive features in the environment. There is no need for preparation of the environment, but the environment must be known in advance. The reliability of this method is not as high as with artificial landmarks.
f. Model Matching In this method information acquired from the robot's onboard sensors is compared to a map or world model of the environment. If features from the sensor-based map and the world model map match, then the vehicle's absolute location can be estimated. Map-based positioning often includes improving global maps based on the new sensory observations in a dynamic environment and integrating local maps into the global map to cover previously unexplored areas. The maps used in navigation include two major types: geometric maps and topological maps. Geometric maps represent the world in a global coordinate system, while topological maps represent the world as a network of nodes and arcs.
This book presents and discusses the state-of-the-art in each of the above six categories. The material is organized in two parts: Part I deals with the sensors used in mobile robot positioning, and Part II discusses the methods and techniques that make use of these sensors.
Mobile robot navigation is a very diverse area, and a useful comparison of different approaches is difficult because of the lack of commonly accepted test standards and procedures. The research platforms used differ greatly and so do the key assumptions used in different approaches. Further difficulty arises from the fact that different systems are at different stages in their development. For example, one system may be commercially available, while another system, perhaps with better performance, has been tested only under a limited set of laboratory conditions. For these reasons we generally refrain from comparing or even judging the performance of different systems or techniques. Furthermore, we have not tested most of the systems and techniques, so the results and specifications given in this book are merely quoted from the respective research papers or product spec-sheets.
Because of the above challenges we have defined the purpose of this book to be a survey of the expanding field of mobile robot positioning. It took well over 1.5 man-years to gather and compile the material for this book; we hope this work will help the reader to gain greater understanding in much less time.