Efficient Evaluation of SLAM Methods and Integration of Human Detection with YOLO Based on Multiple Optimization in ROS2

Abstract

In the realm of robotics, indoor robotics is an increasingly prominent field, and enhancing robot performance stands out as a crucial concern. This research undertakes a comparative analysis of various Simultaneous Localization and Mapping (SLAM) algorithms with the overarching objective of augmenting the navigational capabilities of robots. This is accomplished within an open-source framework known as the Robotic Operating System (ROS2) in conjunction with additional software components such as RVIZ and Gazebo. The central aim of this study is to identify the most efficient SLAM approach by evaluating map accuracy and the time it takes for a robot model to reach its destinations when employing three distinct SLAM algorithms: GMapping, Cartographer SLAM, and SLAM_toolbox. Furthermore, this study addresses indoor human detection and tracking assignments, in which we evaluate the effectiveness of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 models in conjunction with various optimization algorithms, including SGD, AdamW, and AMSGrad. The study concludes that YOLOv8 with SGD optimization yields the most favorable outcomes for human detection. These proposed systems are rigorously validated through experimentation, utilizing a simulated Gazebo environment within the Robot Operating System 2 (ROS2).