VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

Infrasound detection and altitude estimation associated with the December 22, 2020 Yushu fireball

A large bolide was reported at 23:23:33 UTC on December 22, 2020, at a height of ~ 35.5 km at $$31.9^\circ \mathrm{N}$$ 31 . 9 ∘ N , $$96.2^\circ \mathrm{E}$$ 96 . 2 ∘ E in Yushu, Qinghai Province, China. It is the largest fireball observed in China on record with a TNT equivalent of 9.5 kilotons. Infrasound signals were detected by a four-element infrasound array deployed in Yunnan Province, China. The parameters of this event were obtained using the progressive multi-channel correlation method. The altitude of this event was estimated to be $$43.22\pm 15.51\mathrm{ km}$$ 43.22 ± 15.51 km using a ray tracing back-projection algorithm.

Quadcopter altitude estimation using low-cost barometric, infrared, ultrasonic and LIDAR sensors

The goal of this research is to assess the different low-cost sensors for flight altitude measuring of a multirotor UAV at low altitude flight. For optimizing the sensor performances and accuracy, data filtering and other methods were applied. The flight altitude data were collected and stored for later analysis with reference to the true altitude. The correlation coefficient and the mean squared error were calculated in order to assess the sensors' performance. On the basis of the results of the study, it was possible to determine the choice of the adequate sensor for this specific use. The study showed that the best characteristics for this experiment conditions had the Garmin LIDAR-Lite V3HP sensor and the Bosch Sensortech BME280 that combined air humidity, atmospheric pressure, and air temperature sensor.

EFFECT OF AN IN-FLIGHT VERTICAL ACCELEROMETER CALIBRATION ON LANDING ACCURACY AFTER BARO-INERTIAL SYSTEM FAILURE

An issue of improving flight safety during landing with an inertial navigation system (INS) and a failed barometric altimeter is considered. In this paper, we propose a specific algorithm for in-flight calibration of the vertical channel of INS. Accordingly, the baro-inertial integration algorithm using a discrete five-state Kalman filter will be performed during a particular flight maneuver before landing. As a result, it is possible to estimate not only the bias of vertical accelerometer but also its scale factor, which is too small to be defined by a usual in-flight calibration algorithm. After applying the proposed algorithm, the flight management system can provide a safe landing with a standalone INS. The algorithm’s performance is assessed by simulating complete mathematical models of aircraft motion and control systems. The impact of calibrated bias and scale factor of vertical accelerometer on the altitude estimation error is provided through an analysis.

Skip Re-Entry Trajectory Detection and Guidance for Maneuvering Vehicles

The re-entry trajectory of maneuvering vehicles with medium to high hypersonic lift-to-drag ratios is generally planned using quasi-equilibrium flight conditions known from Space Shuttles. They may exhibit an oscillation re-entry phenomenon termed skip re-entry when related components or sensors fail. However, conventional re-entry guidance only considers quasi-equilibrium flights and ignores the possibility of the occurrence of an unexpected skip trajectory; this may lead to the failure of the re-entry mission due to a lack of a corresponding guidance strategy. However, the detection of a skip trajectory is the necessary reference for the decision-making of calling a related guidance algorithm that helps improve the safety of vehicle re-entry. Herein, a skip re-entry detection and trajectory control solution is proposed to play an emergency role in the cases of skip re-entry. Firstly, the oscillation frequency characteristics of the linearized re-entry motion equation of a vehicle are analyzed, and an approximate analytical relationship is constructed for skip altitude estimation. Then, the residual deviation between the altitude feedback data and the estimated skip altitude is calculated and compared with the threshold to determine the occurrence of skip re-entry. In addition, a method for controlling the skip re-entry trajectory with the range extension is developed by controlling the bank angle with a fixed angle of attack profile, satisfying the path constraint requirements. The results indicate that the method effectively performs skip re-entry detection and that it can help extend the range of the vehicles in abnormal re-entry scenarios, keeping the flight within the path constraints and guiding it to the expected location.