Pure dead-reckoning methods such as odometry are prone to drift, and an estimate is needed to reduce the growing unbounded errors [2]. In order to provide a precise position estimation, external sensors, like sonar or laser range finder sensors, are extensively used in robotics, especially in indoor environments [3�C6]. In these sensors, the accuracy is a function of their specifications and the selleck chemical type of features used to represent the environment. Other kinds of commonly used sensors in robotics are cameras, more specifically, monocular, stereo, or trinocular vision systems [7�C12]. In these cases, the accuracy of the sensor is a function of the captured image resolution and the features used in the representation.In general, the structural features commonly found in the environment are assumed to be invariant to height (e.

g., walls, corners, columns). Using this assumption, a planar representation would be adequate for feature extraction Inhibitors,Modulators,Libraries and a distance-based sensor can be used. Among different Inhibitors,Modulators,Libraries types of sensors, 2D laser range finders have been increasing popular during the last decade, because they provide dense Inhibitors,Modulators,Libraries and accurate range measurements with high angular resolution and sampling rates. Figure 1(a) illustrates two classical laser range sensors used in robotics: a LMS200 from SICK, and a HOKUYO URG-04LX. On the other hand, in terms of cost, it is an affordable device for most mobile robotics systems.Figure 1.(a) Two laser range sensors widely used in Robotic: a LMS200 from SICK and a HOKUYO URG-04LX.

(b) Natural landmarks detected and characterized in this work: breakpoints, rupture points, line and curve segments, corners and edges.Once the sensor is chosen, the second task that we must address Inhibitors,Modulators,Libraries is to match the obtained data with the expected data available in a map. To this end, two approaches have been used in mobile robotics: point-based and feature-based matching. Feature-based approaches increase the efficiency and robustness of this process by transforming the acquired raw sensor data into a set of geometric features. Because they are more compact, these feature-based approaches require much less memory than the point-based approaches and can still provide rich and accurate information [13]. Besides, these methods are more robust to the noise resulted from spurious measurements and unknown objects.

Thus, feature-based model is a typical choice for the map representation, which allows the use of multiple models to describe the measurement process for Drug_discovery different parts of the environment.This work extends the CUrvature-BAsed (CUBA) approach for environment description: a feature-based approach proposed by N��?ez et al. LDK378 [14�C16]. In these previous works, the authors present a feature-based approach which employs multiple models to characterize the environment.