Comparison of Gmapping and Hector technology on Indoor mobile robot SLAM using Lidar sensor

Abstract

In recent years, as artificial intelligence has become a popular research direction in various fields, the intelligence of mobile robots has also developed gradually with the progress of the times. The mobile robots are aware of the surrounding information and can move autonomously as a requirement of intelligence. Simultaneous Localization and Mapping (SLAM) is the key to accomplish this intelligence requirement. In this paper, LIDAR is used as the main sensor and a Raspberry Pi-based mobile robot is used as a platform to study the autonomous navigation and obstacle avoidance functions of the robot in indoor environment. The main research elements are as follows. Firstly, the background related to mobile robot and SLAM research technology is introduced, and the mobile robot hardware platform and its configuration used in this paper are introduced, the hardware platform for simultaneous localization and mapping construction is built, and the motion model and sensor observation model of the mobile robot are established. The environment map of the mobile robot was constructed, and the coordinate conversion method and the algorithm construction of 2D raster map were studied to prepare for the SLAM navigation and obstacle avoidance functions in the future. The problems of laser SLAM based on particle filtering and laser SLAM based on graph optimization are investigated. The advantages and disadvantages of different algorithms for constructing maps in the same environment are compared. The simulation environment was set up in Gazebo to perform simulation experiments on the respective representative SLAM parties Gmapping and Hector. After comparison experiments, the algorithm that is more suitable for the research laboratory environment is selected. Meanwhile, the A* algorithm based on global path planning and DWA algorithm based on local path planning are combined with Monte Carlo localization method, and the simulation experiments of the navigation scheme based on the map built by SLAM are conducted in the built simulation environment to verify the feasibility of its autonomous navigation function. Finally, the overall scheme of SLAM and navigation for mobile robots proposed in this paper is validated in a laboratory environment. A map is constructed using a better-quality Hector SLAM and navigation experiments with formulated targets are performed on this map. Experiments show that the mobile robot in this scheme can achieve autonomous obstacle avoidance and successfully reach the target point in a laboratory environment. The feasibility of the navigation scheme is confirmed. A complete mobile robot navigation system has been built by carefully studying each key part of SLAM and navigation technology.