Open AccessA novel SLAM framework based on 2D LIDAR
Author(s) -
Songbo Wu,
Jingyu Lin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/2/022104
Subject(s) - occupancy grid mapping , simultaneous localization and mapping , odometer , computer science , lidar , mobile robot , artificial intelligence , computer vision , kalman filter , grid reference , extended kalman filter , grid , line (geometry) , global map , robot , geography , mathematics , remote sensing , geometry , geodesy
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.