Parsing Indoor Manhattan Scenes Using Four-Point LiDAR on a Micro UAV

We propose the first 3D mapping algorithm using four-point LiDAR for a micro unmanned aerial vehicle (UAV). Existing mapping approaches depend on 360° 2D laser scanner and RGB-D camera, which are unsuitable for micro UAV with a small payload. The proposed method builds a 3D structure map with an accumulated point cloud obtained from low-cost and lightweight four ToF sensors suitable for micro UAV in four directions: front, back, left, and right. The noise of range measurement by the low-cost ToF sensor and inaccurate 6-DoF pose estimation of Crazyflie make a noisy point cloud. We overcome these problems by utilizing the geometric constraints of the interior structures, the Manhattan world (MW), and the proposed method successfully parse the floor plan of the Manhattan scenes. We evaluate the proposed method in various MW structures and demonstrate that the proposed method produces comparable results to the ROS Gmapping algorithm, which uses a 360° 2D laser scanner.