Where am I? Sensors and Methods for Mobile Robot Positioning

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214 Part II Systems and Methods for Mobile Robot Positioning 9.4.1 Three-Dimensional Geometric Model-Based Positioning Fennema et al. [1990] outlined a system for navigating a robot in a partially modeled environment. The system is able to predict the results of its actions by an internal model of its environment and models of its actions. Sensing is used to correct the model's predictions about current location or to progress towards some goal. Motions are composed in a hierarchical planner that sketches overall paths and details the short term path. Control of the robot is broken down into the action level, the plan level, and the goal level. Landmarks are chosen to measure progress in the plan. The system must receive perceptual confirmation that a step in the plan has been completed before it will move to the next part of the plan. Later steps in a plan expand in detail as earlier steps are completed. The environment is modeled in a graph structure of connected nodes called locales. Locales may exist at a variety of scales in different hierarchies of the map. Other information is kept in the system associated with each locale to provide more detail. Using these models the robot operates in a planand monitor-cycle, confirming and refining plans to achieve overall goals. The algorithm by Fennema et al. [1990] matches images to the map by first matching the twodimensional projection of landmarks to lines extracted from the image. The best fit minimizes the difference between the model and the lines in the data. Once the correspondence between model and two-dimensional image is found, the relation of the robot to the world coordinate system must be found. This relation is expressed as the rotation and translation that will match the robot- and worldsystems. Matching is done by considering all possible sets of three landmarks. Once a close correspondence is found between data and map, the new data is used to find a new estimate for the actual pose. Kak et al. [1990] used their robot's encoders to estimate its position and heading. The approximate position is used to generate a two-dimensional scene from their three-dimensional world model and the features in the generated scene are matched against those extracted from the observed image. This method of image matching provides higher accuracy in position estimation. Talluri and Aggarwal [1991; 1992] reported their extensive work on model-based positioning. They use three-dimensional building models as a world model and a tree search is used to establish a set of consistent correspondences. Talluri and Aggarwal [1993] wrote a good summary of their algorithms as well as an extensive survey of some other vision-based positioning algorithms. sang06.cdr, .wmf Figure 9.6: Finding a location on a digital elevation maps (DEM) that matches a visual scene observed from a point. The 'x' marks a possible location in the DEM that could generate the observed visual scene to the right.
Chapter 9: Vision-Based Positioning 215 9.4.2 Digital Elevation Map-Based Localization For outdoor positioning, Thompson et al. [1993] developed a hierarchical system that compares features extracted from a visual scene to features extracted from a digital elevation maps (DEM). A number of identifiable features such as peaks, saddles, junctions, and endpoints are extracted from the observed scene. Similarly, features like contours and ridges are extracted from the DEM. The objective of the system is to match the features from the scene onto a location in the map. The feature matching module interacts with each feature extractor as well as with a geometric inference module. Each module may request information and respond to the others. Hypotheses are generated and tested by the interaction of these feature extractors, geometric inference, and feature matching modules. In order to make matching more tractable, configurations of distinctive and easily identified features are matched first. Using a group of features cuts down dramatically on the number of possible comparisons. Using rare and easily spotted features is obviously advantageous to making an efficient match. A number of inference strategies that express viewpoint constraints are consulted in the geometric inference module. These viewpoint constraints are intersected as more features are considered, narrowing the regions of possible robot location. Sutherland [1993] presented work on identifying particular landmarks for good localization. A function weighs configurations of landmarks for how useful they will be. It considers the resulting area of uncertainty for projected points as well as relative elevation. Sutherland showed that a careful choice of landmarks usually leads to improved localization. Talluri and Aggarwal [1990] formulated position estimation using DEM as a constrained search problem. They determined an expected image based on a hypothetical location and compared that to the actual observed image. Possible correspondences are eliminated based on geometric constraints between world model features and their projected images. A summary of their work is given in [Talluri and Aggarwal, 1993]. 9.5 Feature-Based Visual Map Building The positioning methods described above use a priori information about the environment in the form of landmarks, object models or maps. Sometimes pre-loaded maps and absolute references for positions can be impractical since the robot's navigation is restricted to known structured environments. When there is no a priori information, a robot can rely only on the information obtained by its sensors. The general framework for map-building has been discussed in the previous chapter. For constructing the environment model, vision systems usually use image features detected at one or more robot positions. According to the computer vision theory of structure from motion and stereo vision, correct correspondences of image features detected in several locations can provide information about the motion of the sensor (both translation and rotation), as well as of the threedimensional structure of the environment at the feature locations. The trajectory of the sensor can be obtained by visual dead-reckoning, i.e., the integration of the estimated incremental motion. This is illustrated in Fig. 9.7. The object features detected in a sensor location become the relative reference for the subsequent sensor locations. When correspondences are correctly established, vision methods can provide higher
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Acknowledgments This research was s
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2.4.2.2 Watson Gyrocompass ........
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6.4 Summary .......................
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INTRODUCTION Leonard and Durrant-Wh
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Part I Sensors for Mobile Robot Pos
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CHAPTER 5 ODOMETRY AND OTHER DEAD-R
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Page 236 and 237: REFERENCES 1. Abidi, M. and Chandra
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