Accurate and reliable maps generated autonomously guarantees improved localization especially in GPS denied surroundings like indoor (Hough, P.V.C, 1959). The use of odometry is not sufficient for position estimation due unbounded position errors. Therefore, since office like environments consists of planar surfaces, a 2D space model is adequate to describe the robot surroundings because objects are predominantly straight line segments and right angle corners. Coincidentally, line segments and corner representation are the two most popular methods for indoor modelling from a laser rangefinder. The focus in this paper however is corner extraction methods. A number of line and corner extraction techniques first transform scan data into Cartesian space then a linear regression method or corner extraction algorithm is applied. Some algorithms employ Hugh transform (Hough, P.V.C, 1959). & (Duda, R. O, 1972) a popular tool for line detection from scan data due to its robustness to noise and missing data. It works in sensor measurement space. However, the computational cost associated to its voting mechanism renders real-time implementation impossible. On the other hand, an early work by (Crowley, J, 1989) paved the way to subsequent line extraction methods from a range sensor. In their work, a process for extracting line segments from adjacent co-linear range measurements was presented. The Kalman filter update equations were developed to permit the correspondence of a line segment to the model to be applied as a correction to estimated position. The approach was recently extended by (Pfister, S.T et al. 2003), first providing an accurate means to fit a line segment to a set of uncertain points via maximum likelihood formalism. Then weights were derived from sensor noise models such that each point's influence on the fit is according to its uncertainty. Another interesting work is one by (Roumeliotis & Bekey, 2000), where two Extended Kalman filters are used to extract lines from the scan data. In the algorithm, one Kalman filter is used to track the line segments while the other estimates line parameters. The combination of the two filters makes it possible to detect edges and straight line segments within the sensor field of view. There are many features types one can extract from a laser sensor, and are dependent on the obstacles found in the room. If the room has chair and table, one would be tempted to extract point features from their legs. Size, shape and texture of objects contribute to the type of feature to extract from the sensor. The use of generalised algorithms is not uncommon, i.e. algorithms which extract lines from wall, point features from table legs and arcs to categorise circular objects (Mendes, & Nunes, 2004). The parameters that distinguish each extracted feature makes up the map or state estimate. The key to a successful robot pose estimation lies in its ability to effectively extract useful information about its location from observations (Li & Jilkov, 2003). Therefore we proposed an improved c...