  For effective autonomous navigation,estimation of the pose of the robot is
essential at every sampling time. For computing an accurate
estimation,odometric error needs to be reduced with the help of data from
external sensor. In this work, a technique has been developed for accurate pose
estimation of mobile robot by using Laser Range data. The technique is robust
to noisy data, which may contain considerable amount of outliers. A grey image
is formed from laser range data and the key points from this image are
extracted by Harris corner detector. The matching of the key points from
consecutive data sets have been done while outliers have been rejected by
RANSAC method. Robot state is measured by the correspondence between the two
sets of keypoints. Finally, optimal robot state is estimated by Extended Kalman
Filter. The technique has been applied to an operational robot in the
laboratory environment to show the robustness of the technique in presence of
noisy sensor data. The performance of this new technique has been compared with
that of conventional ICP method. Through this method, effective and accurate
navigation has been achieved even in presence of substantial noise in the
sensor data at the cost of a small amount of additional computational
complexity.
