This paper presents the design of a two key-frame visual inertial navigation system (2KF-VINS) using combined Lie group SE2(3) extended Kalman filter (EKF) design framework. The conventional 2KF-VINS filter is unobservable for translations along all three axis and rotation about the gravity direction. As a result, the filter suffers from estimation inconsistencies related to unobservable transformations of the estimation problem. The proposed combined Lie group SE2(3) framework remedies this issue by implicitly preserving the observability consistency property of the filter. Monte Carlo numerical simulations are used to validate the theoretical performance of the right -SE2(3) 2KF-VINS, along with experimental validation using EuRoC MAV dataset to evaluate the performance in real-world scenarios. Additionally, the proposed algorithm is compared with state of the art MSCKF VINS, right-invariant MSCKF VINS, left-SO(3) and right-SO(3) 2KF-VINS versions with identical and realistic tuning parameters in order to validate the performance related to accuracy, consistency, and computational speed of the method.

This paper presents a unique outdoor aerial visual-inertial-LiDAR dataset captured using a multi-sensor payload to promote the global navigation satellite system (GNSS)-denied navigation research. The dataset features flight distances ranging from 300m to 5km, collected using a DJI M600 hexacopter drone and the National Research Council (NRC) Bell 412 Advanced Systems Research Aircraft (ASRA). The dataset consists of hardware synchronized monocular images, IMU measurements, 3D LiDAR point-clouds, and highprecision real-time kinematic (RTK)-GNSS based ground truth. Ten datasets were collected as ROS bags over 100 mins of outdoor environment footage ranging from urban areas, highways, hillsides, prairies, and waterfronts. The datasets were collected to facilitate the development of visual-inertial-LiDAR odometry and mapping algorithms, visual-inertial navigation algorithms, object detection, segmentation, and landing zone detection algorithms based upon real-world drone and fullscale helicopter data. All the datasets contain raw sensor measurements, hardware timestamps, and spatio-temporally aligned ground truth. The intrinsic and extrinsic calibrations of the sensors are also provided along with raw calibration datasets. A performance summary of state-of-the-art methods applied on the datasets is also provided.