DescriptionThere is a growing demand for performing high precision tasks in indoor environments. Using a LiDAR to map the environment alongside a SLAM algorithm called LOAM, developed by Dr. Zhang from CMU, environments can be mapped with low computational complexity. The use of this algorithm and sensor is tested in indoor environments to assess the performance and viability of indoor mapping with the Velodyne VLP-16 LiDAR. The experiment is tailored so that it mimics certain behaviors of a mobile robot in the hopes that the conclusion of this experiment can be generalized to mobile robots. Results of this experiment produced highly accurate clouds that were replicas to the real-world environment with an accuracy as high as 99.71%. Large indoor environments were also mapped (above 100 meters in length) with drift less than 1 meter in the best scenario. These results verify that accurate point cloud generation in indoor environments is viable and can be useful for mobile robots.