Simultaneous localization and mapping for semi sparse point clouds

Nowadays, needs to have high density with highest possible detailed maps is increasing exponentially. However, providing such information in classical approaches demands to spend a considerable amount of time for localizing the sensor in a desired coordinate frame and then mapping the environment. Simultaneous Localization And Mapping (SLAM) algorithm is capable of localizing the sensor and do the mapping while the sensor is moving through the environment.In this paper, SLAM will be applied on the data of a lightweight 3D laser scanner in which we call semi-sparse point cloud, because of the unique specifications of the point cloud which is because of various resolution in vertical and horizontal directions. Also, in contrast to most of the SLAM algorithms, there is no aiding sensor to provide prior information of motion. The output of the algorithm would be a high-density full geometry detailed map in the shortest possible time.The algorithm has been tested in medium scale indoor and outdoor environments. Also, accuracy assessment has been done by simulation in Gazebo and ROS. Considering the scanner (Velodyne Puck)accuracy as 3cm, the map was generated with sub-decimeter accuracy.