Where am I? Sensors and Methods for Mobile Robot Positioning

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206 Part II Systems and Methods for Mobile Robot Positioning 8.4 Summary Map-based positioning is still in the research stage. Currently, this technique is limited to laboratory settings and good results have been obtained only in well-structured environments. It is difficult to judge how the performance of a laboratory robot scales up to a real world application. Kortenkamp and Weymouth [1994] noted that very few systems tested on real robots are tested under realistic conditions with more than a handful of places. We summarize relevant characteristics of map-based navigation systems as follows: Map-based navigation systems: C are still in the research stage and are limited to laboratory settings, C have not been tested extensively in real-world environments, C require a significant amount of processing and sensing capability, C need extensive processing, depending on the algorithms and resolution used, C require initial position estimates from odometry in order to limit the initial search for features to a smaller area. There are several critical issues that need to be developed further: C Sensor selection and sensor fusion for specific applications and environments. C Accurate and reliable algorithms for matching local maps to the stored map. C Good error models of sensors and robot motion. C Good algorithms for integrating local maps into a global map.
Chapter 9: Vision-Based Positioning 207 CHAPTER 9 VISION-BASED POSITIONING A core problem in robotics is the determination of the position and orientation (often referred to as the pose) of a mobile robot in its environment. The basic principles of landmark-based and map-based positioning also apply to the vision-based positioning or localization which relies on optical sensors in contrast to ultrasound, dead-reckoning and inertial sensors. Common optical sensors include laser-based range finders and photometric cameras using CCD arrays. Visual sensing provides a tremendous amount of information about a robot's environment, and it is potentially the most powerful source of information among all the sensors used on robots to date. Due to the wealth of information, however, extraction of visual features for positioning is not an easy task.The problem of localization by vision has received considerable attention and many techniques have been suggested. The basic components of the localization process are: C representations of the environment, C sensing models, and C localization algorithms. Most localization techniques provide absolute or relative position and/or the orientation of sensors. Techniques vary substantially, depending on the sensors, their geometric models, and the representation of the environment. The geometric information about the environment can be given in the form of landmarks, object models and maps in two or three dimensions. A vision sensor or multiple vision sensors should capture image features or regions that match the landmarks or maps. On the other hand, landmarks, object models, and maps should provide necessary spatial information that is easy to be sensed. When landmarks or maps of an environment are not available, landmark selection and map building should be part of a localization method. In this chapter, we review vision-based positioning methods which have not been explained in the previous chapters. In a wider sense, “positioning” means finding position and orientation of a sensor or a robot. Since the general framework of landmark-based and map-based positioning, as well as the methods using ultrasound and laser range sensors have been discussed, this chapter focuses on the approaches that use photometric vision sensors, i.e., cameras. We will begin with a brief introduction of a vision sensor model and describe the methods that use landmarks, object models and maps, and the methods for map building. 9.1 Camera Model and Localization Geometric models of photometric cameras are of critical importance for finding geometric position and orientation of the sensors. The most common model for photometric cameras is the pin-hole camera with perspective projection as shown in Fig. 9.1. Photometric cameras using optical lens can be modeled as a pin-hole camera. The coordinate system (X, Y, Z) is a three-dimensional camera coordinate system, and (x, y) is a sensor (image) coordinate system. A three-dimensional feature in
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INTRODUCTION Leonard and Durrant-Wh
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Part I Sensors for Mobile Robot Pos
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256 References 309. EATON - Eaton-K
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258 References 349. SPSi - Spatial
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