Laser-aided Infrared Visual Inertial Odometry for Unmanned Aerial Vehicle with Multi-plane Constraint

In this work, we propose an infrared visual inertial odometry (VIO) algorithm for unmanned aerial vehicles (UAV) for both daytime and nighttime. It utilizes images from a single downward-looking thermal camera, and uses a laser scanner to measure relative distance to determine the scale factor. The scale recovery process consists of a comprehensive logic flow to try out measurements into a homography decomposition with a multi-plane constraint, obtaining the results with minimum error, making the algorithm more robust. Moreover, other modifications, such as keyframe idea and pruning KLT tracker with sub-regions further improves the performance. Besides, the results are fused with an on-board IMU through an EKF-based sensor fusion framework. This infrared visual inertial odometry algorithm can still navigate UAVs during the missions when GPS is down or not reliable or under poor illumination condition.