A novel SLAM framework based on 2D LIDAR

Abstract

Abstract SLAM is a basic problem in many mobile robot applications. The most commonly used SLAM framework is too complex, and many 3D data algorithms are not suitable for 2D LIDAR. In order to solve this problem, we propose a novel SLAM framework which is more in line with embedded platform. In our framework, we use the classic ICP algorithm as the odometer to calculate the pose of the mobile robot, and use Kalman filter as optimization to remove the accumulating drift. In the aspect of map generation, we first generate the occupancy grid map, then transform occupancy grid map to binary map as the final environment map. We build a simulation platform based on MTLAB to verify the feasibility and effectiveness of our proposed framework.