Robotic Navigation Unveiled:A Comprehensive Study of GMapping, Hector Slam, and Cartographer

摘要

Robots are able to create detailed maps of their surroundings and determine their own location within them because to a technique called Simultaneous Localization and Mapping (SLAM). As the robot moves around its environment, the complex system generates and updates a map using sensor data from cameras, laser range finders, and other devices. There are several SLAM algorithms available, and they differ in terms of accuracy and processing efficiency, therefore comparisons are necessary. One of the main goals of these assessments is to identify the approach that performs optimally for a certain use case. In this work, three well-known SLAM algorithms are assessed: Hector GMapping, Cartographer, and SLAM. To make this evaluation easier, this research makes use of the Robot Operating System (ROS) in combination with RVIZ and Gazebo software. This study's main goals are to measure how long it takes the Burger robot model to navigate a new interior environment and to carefully assess the accuracy of the maps produced by each algorithm. The research's conclusions and observations provide insightful advice for choosing the best SLAM algorithm for practical robotic applications.