Visual SLAM with line and corner features

We propose a new vision-based SLAM (Simultaneous Localization and Mapping) technique using both line and corner features as landmarks in the scene. The proposed SLAM algorithm uses an Extended Kalman Filter based framework to localize and reconstruct 3D line and corner landmarks at the same time and in real time. It provides more accurate localization and map building results than conventional corner feature only-based techniques. Moreover, the reconstructed 3D line landmarks enhance the performance of the robot relocation when robot's pose remains uncertain with corner information only. Experimental results show that the hybrid landmark based SLAM, using lines and corners, produces better performance than corner only one's.