Mobile robot positioning: Sensors and techniques

Abstract

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 brevity, not all could be cited in this article.

Robot Localization Based on Visual Landmarks

“…Errors in odometry are caused by the intersection between the wheels and the terrain, for example slippage, cracks, debris of solid material, etc. (Borenstein et al, 1997). We add a random noisy angle θ Δ of degree to each actual angle.…”

“…Researchers have developed a variety of systems sensors, and techniques for mobile robot positioning. (Borenstein et al, 1997) define seven categories for positioning system: Odometry, Inertial Navigation, Magnetic Compasses, Active Beacons (Trilateration method and Triangulation method), Global Positioning Systems, Landmark Navigation, and Model Matching. Some experimental systems that work on localization are using mobile robots equipped with sensors that provide range and bearing measurements to beacons (Witkowski and Rückert, 2002) and some other work with vision sensors (Chinapirom et al, 2004).…”

“…Because of the lack of a single good method, developers of mobile robots usually combine two or more methods (Borenstein et al, 1997). To predict the robot's location, some systems combine odometric measurements, landmark matching, and triangulation with observations of the environment from a camera sensor (DeSouza and C.Kak, 2002;Yuen et al, 2005).…”

Towards Semantically Intelligent Robots

“…Cascade control systems for localization are more reliable in this sense. J. Borenstein et al (Borenstein et al, 1997) defined seven categories for positioning systems based on the type of sensors used in controlling the robot. 1) Odometry is based on simple equations which hold true when wheel revolutions can be translated accurately into linear displacement relative to the floor.…”

Hand Motion Tracking System using Inertial Measurement Units and Infrared Cameras

“…This bias o↵set error produces a severe orientation tracking error called "drift", which grows proportional to time. The drift is a common phenomenon in orientation tracking, which causes several problems in navigation and other applications that utilize commercial-grade MEMS gyroscopes to determine the orientation of the vehicles, robot arms or any objects [8], [9].…”