Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment

Abstract

With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual–inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

Autonomous Landing of Quadrotor Unmanned Aerial Vehicles Based on Multi-Level Marker and Linear Active Disturbance Reject Control

“…An ArUco-based marker was used for scale estimation as well as visual odometry. At the same time, the long-term drift problem was reduced [ 4 ].…”

“…The ArUco is a composite square marker composed of a wide black border and an internal binary matrix that determines its identifier [ 4 ]. One or more ArUco markers may be contained in an image captured by a camera on the UAV.…”

“…Many markers with different shapes are designed to guide UAV landing, such as H-shaped, T-shaped, QR codes, AprilTags, ArUco, and so on [ 4 ]. Furthermore, the scale and number of markers are also important for precise landing, especially in dynamic scenes.…”

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

“…For this reason, they used the spherical radial rule to approximate the state posterior distribution in the optimal framework, and then proposed the Cubature Kalman Filter (CKF), but the CKF has higher computational complexity and requires more sampling and operations, resulting in poor real−time performance, which is not suitable for some applications with high real−time performance requirements. The extended Kalman filter thus remains the mainstream state estimation algorithm, and developing a low−complexity filter with high accuracy is still challenging [ 20 , 21 ].…”