Gradient model based feature extraction for simultaneous localization and mapping in outdoor applications

In this paper a feature detection algorithm based on a new curve gradient model is proposed for simultaneous localization and mapping (SLAM) for complex outdoor environments. The curve gradient model is derived for data segmentation and has the advantage of being suitable for segmentation of data from various types of feature such as point feature and circular feature. The real time implementation of SLAM together with this feature extraction algorithm is realized by using a combination of odometry and laser scanner data. The system was tested on a long walk way at Nanyang Technological University. The experimental results show that the feature detection algorithm performs well during SLAM.