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• W1570254668 abstract "Navigation is one of the basic problems for autonomous mobile robots. Its history can be traced back to long time ago. Today, navigation is a well-understood quantitative science, used routinely in maritime and aviation applications [Adams, 1999]. Given this, the question must be asked as to why robust and reliable autonomous mobile robot navigation remains such a difficult problem. The core of the problem is the reliable acquisition or extraction of information about navigation beacons from sensor information and the automatic correlation or correspondence of these with some navigation map [Guivant et al., 2000]. Many navigation systems use artificial beacons to realize their navigation task, but the approach may not be realistic in applications such as exploration of jungles or other unknown environments. In this situation, one needs to utilize naturally occurring structure of typical environments to achieve a similar performance. Hence, fast and reliable algorithms capable of extracting features from a large set of noisy data are important in such applications. Some of the early efforts in this direction have focused on extracting line features in an indoor environment based on the information provided by sonar and laser sensors. In [Crowley, 1985], a least-squares line fitting technique was applied to extract edges from ultrasonic sensor data. In [Taylor & Probert, 1996], a recursive line fitting system is used to extract line segments under polar coordinates and an ellipse fitting method is also implemented for data from a laser sensor. In [Vandorpe et al., 1996], line segments are detected using a regression least-squares parameter estimation method whereas the center and radius of a circle feature are estimated based on the average value of the measurements of the circle from a 2D range scanner. Later, a two-layer Kalman filter was used to calculate the parameters of a line by an on-line method in [Roumeliotis & Bekey, 2000]. Observe that the aforementioned articles are focused on indoor applications and are mainly concerned with line extraction. For an outdoor environment, the problem of feature selection and detection is more challenging. In our view, in most typical semi-structured outdoor environments, such as campuses, parks and suburbs, tree trunks and tree-like objects, such as pillars, are relatively stable, regular and naturally occurring features that can provide very useful information for mobile robot navigation. Recently, some research on the use of these kinds of geometrical features has been carried out in [Guivant et al., 2002]. Also, [Guivant et al., 2002; Bailey, 2002] addressed the problem of extracting tree trunks from laser scan data where the centre and radius of a circle are estimated by averaging the measurements. This method can be" @default.
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• W1570254668 date "2008-06-01" @default.
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• W1570254668 title "A Novel Feature Extraction Algorithm for Outdoor Mobile Robot Localization" @default.
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• W1570254668 doi "https://doi.org/10.5772/5992" @default.
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