Simulation and Implementation of SLAM Drawing Based on ROS Wheeled Mobile Robot

In order to realize the establishment of the unknown terrain map in the indoor environment of the ROS wheeled mobile robot, this article uses Lidar, Raspberry Pi and other hardware to build the wheeled mobile robot, in the ROS environment, the robot is built by the URDF model, and the robot model is...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-04, Vol.1865 (4), p.42068
Main Authors: Cui, Yufan, Niu, Zijie
Format: Article
Language:English
Subjects:
Algorithms
Barriers
Cartography
Construction
Evaluation
Image processing
Indoor environments
Keyboards
Optimization
Optimization algorithms
Physics
Robots
Simulation
Terrain mapping
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Summary:In order to realize the establishment of the unknown terrain map in the indoor environment of the ROS wheeled mobile robot, this article uses Lidar, Raspberry Pi and other hardware to build the wheeled mobile robot, in the ROS environment, the robot is built by the URDF model, and the robot model is configured. Differential controller, keyboard control node, etc. Establish the simulation environment, use different simulation environments to visually simulate the robot, and simulate the two optimization algorithms in the SLAM algorithm to simulate the construction of the map. In order to explore the effect of SLAM’s different optimization algorithms on the construction of graphics, the SLAM algorithm based on filter and the SLAM algorithm based on graph optimization are introduced respectively. The robot is selected. After the robot is built, three different experimental environments are selected. A variety of map-building algorithms are used to experiment, comparing the effects of G-mapping algorithm and Cartographer algorithm to build maps in a small-scale, large-scale and multi-obstacle environment. Use the image processing software under the Linux system to analyse the map created by the robot in the experimental environment, measure the coordinates of the robot feature points in the created map by the number of pixels, and compare the actual coordinates of the location of the special point in the actual environment to evaluate Construction effect. By comparing the effects of the two algorithms on the contours of various obstacles when building a map, the effect of mapping in a multi-obstacle environment is evaluated. The results show that the SLAM graph-building algorithm based on graph optimization has a more stable graph-building effect.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1865/4/042068