<![CDATA[Quadcopter is an Unmanned Aerial Vehicle (UAV) that is deployed to operate in areas that are not maximally accessible by Unmanned Ground Vehicles (UGV) in geographic structures that have been distorted due to natural disasters. Quadcopter requires the ability to recognize the surrounding environment by using a map. A map is a set of features that describe the environment such as walls, obstacles, landmarks, etc. Maps are relatively easy to make in a static environment, but in a disaster-damaged environment, maps will be more difficult to create because the environment has changed. The solution to this problem is that the quadcopter must be able to build its own environmental map. To build a map, a mapping process is needed that can be done using Simultaneous Localization and Mapping (SLAM). Hector SLAM is one of the SLAM algorithms which works based on scan matching technique and without odometer. Simulations were carried out to test the 2D mapping results from the Hector SLAM algorithm. The mapping was carried out with a LiDAR sensor embedded in the quadcopter and tested in 3 different environments. Simulations were carried out with 3D Gazebo and Rviz simulators based on Robot Operating System (ROS). There are 36 test scenarios carried out with the best map accuracy obtained with a Structural Similarity Index (SSIM) value of 0.78, Mean Squared Error (MSE) value of 5344.1, and Pixel Matching percentage of 89.59%.]]>
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