  An accurate and computationally efficient SLAM algorithm is vital for modern
autonomous vehicles. To make a lightweight the algorithm, most SLAM systems
rely on feature detection from images for vision SLAM or point cloud for
laser-based methods. Feature detection through a 3D point cloud becomes a
computationally challenging task. In this paper, we propose a feature detection
method by projecting a 3D point cloud to form an image and apply the
vision-based feature detection technique. The proposed method gives repeatable
and stable features in a variety of environments. Based on such features, we
build a 6-DOF SLAM system consisting of tracking, mapping, and loop closure
threads. For loop detection, we employ a 2-step approach i.e. nearest
key-frames detection and loop candidate verification by matching features
extracted from rasterized LIDAR images. Furthermore, we utilize a key-frame
structure to achieve a lightweight SLAM system. The proposed system is
evaluated with implementation on the KITTI dataset and the University of
Michigan Ford Campus dataset. Through experimental results, we show that the
algorithm presented in this paper can substantially reduce the computational
cost of feature detection from the point cloud and the whole SLAM system while
giving accurate results.
