Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments.
Model-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification Neural Network