Hector SLAM based Simulation for Path Planning and Orientation for Aerial Drones

This paper addresses the challenge of autonomous robot navigation by implementing the Hector SLAM algorithm within the Robot Operating System(ROS) framework. Gmapping package is employed for mapping by fusing laser and odometry data . Unique feature of this system is its integration with ROS and Gmapping using Hector slam ,allowing an effective and precise path planning process. An Adaptive Monte Carlo Localization (AMCL) method achieved a pose error of 0.1 meters with a convergence rate of 90% .Efficient path planning has achieved success rate of 80%. The system's performance is validated through successful autonomous navigation, mapping, and visualization in a simulated environment.This research empowers robotics applications, offering practical benefits for automation, surveillance, and various industries reliant on autonomous navigation.