A multi-sensor fusion SLAM algorithm for indoor aerial robots

Nowdays, existing Light Detection and Ranging (LiDAR) based unknown-space-exploration Simultaneous Localization and Mapping (SLAM) methods have the problems of unstable mapping in feature-degraded environments and poor estimation accuracy in the vertical direction. To solve these problems, this study presents a multi-sensor fusion SLAM algorithm for indoor aerial robots as well as Unmanned Aerial Vehicles (UAVs). The sensors used in the multi-sensor fusion algorithm include optical flow sensor, barometer, Inertial Measurement Unit (IMU), and LiDAR. The proposed algorithm is named LiDAR-IMU-optical flow odometry fusion algorithm (Lioo). We derived and established a tightly coupled multi-sensor fusion framework based on factor graph optimisation. The IMU-optical flow pre-integration factor is proposed, and the barometer factor to the algorithm is added innovatively. Engineering applications are also realised in this paper. Flight experiments demonstrated that the proposed algorithm achieves more precise mapping and localisation of UAVs in indoor environments.