Enhanced UAV pose estimation using a KF: experimental validation

— While a Global Positioning System (GPS) is the most widely used sensor modality for aircraft navigation; researchers have been motivated to investigate other navigational sensor modalities because of the desire to operate in GPS denied environments. Due to advances in computer vision and control theory, monocular camera systems have received growing interest as an alternative/collaborative sensor to GPS systems. Cameras can act as navigational sensors by detecting and tracking feature points in an image. Current methods have a limited ability to relate feature points as they enter and leave the camera field of view (FOV). A vision-based position and orientation estimation method for aircraft navigation and control is described. This estimation method accounts for a limited camera FOV by releasing tracked features that are about to leave the FOV and tracking new features. At each time instant that new features are selected for tracking, the previous pose estimate is updated. The vision-based estimation scheme can provide input directly to the vehicle guidance system and autopilot. Simulations are performed wherein the vision-based pose estimation is integrated with a nonlinear flight model of an aircraft. Experimental verification of the pose estimation is performed using the modelled aircraft.

OCEANS 2017 - Aberdeen, 2017

o estimate the pose of an unmanned aerial vehicle (UAV) during the landing process aboard a ship is used a vision system based on a standard RGB camera using one workstation for processing data. It is used a ground-based vision system to allow the use of a small size and weight UAV, due to the low computer requirements onboard. The resampling step in the particle filter takes a decisive role in the obtained pose estimation, and ten traditional resampling steps are tested and compared with a developed resampling strategy inspired by the genetic algorithms. The obtained results show that a classical resampling step is not sufficient in this problem and gets easily stuck in local minima. Those local minima are originated by the high dimensional search space and from the employed observation likelihood metric, which is very dependent on the UAV geometry which generates ambiguity in the pose estimate for complementary poses (poses with large wing pixel overlap, where the majority of the UAV area is concentrated). Results show that the errors obtained are lower and much more compatible with the requirements for this kind of problems when compared to the common existing resampling strategies.

AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005

In this paper, we present a versatile framework to enable autonomous flights of a Micro Aerial Vehicle (MAV) which has only slow, noisy, delayed and possibly arbitrarily scaled measurements available. Using such measurements directly for position control would be practically impossible as MAVs exhibit great agility in motion. In addition, these measurements often come from a selection of different onboard sensors, hence accurate calibration is crucial to the robustness of the estimation processes. Here, we address these problems using an EKF formulation which fuses these measurements with inertial sensors. We do not only estimate pose and velocity of the MAV, but also estimate sensor biases, scale of the position measurement and self (inter-sensor) calibration in real-time. Furthermore, we show that it is possible to obtain a yaw estimate from position measurements only. We demonstrate that the proposed framework is capable of running entirely onboard a MAV performing state prediction at the rate of 1 kHz. Our results illustrate that this approach is able to handle measurement delays (up to 500ms), noise (std. deviation up to 20 cm) and slow update rates (as low as 1 Hz) while dynamic maneuvers are still possible. We present a detailed quantitative performance evaluation of the real system under the influence of different disturbance parameters and different sensor setups to highlight the versatility of our approach. 1 or generally, an external system measuring the 3D position of the MAV

Journal of Mechatronics and Robotics, 2021

Sensor outputs are used when finding the position of an Autonomous Aerial Vehicle (UAV). Outputs such as air pressure, vehicle acceleration or magnetic field help the vehicle decide its attitude and altitude. However, it must also predict the next movement of the vehicle. The Kalman Filter is used to assist observing and predicting the state of a system. To achieve this, sensors and a model that can predict the future movement of the vehicle are needed. In practice, the sensors and models are not perfect. There are always uncertainties like weather conditions. Also the data from the sensors are often noisy. Getting less noisy data for an unmanned aerial vehicle is important for stable flight. This uncertainty is reduced with the Kalman Filter. The mentioned uncertainties can be reduced using the Kalman Filter and more stable data can be obtained.

Unmanned Systems Technology XIV, 2012

This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ∼1.9% of the path length.

Proceedings of the 16th IFAC World Congress, 2005, 2005

Part-time studies of the MSc program as source of number of technical and scientific innovations in Automation and Computer Based Systems Engineering at Faculty of Electrical Engineering of SS Cyril and Methodius University in Skopje, are run for more than 15 years. This innovation in Unmanned Aerial Vehicles (UAV's) is the most recent one. By definition, navigation is a process of determining the navigational parameters of the centre of mass, of the moving object. The system that provides us the navigational parameters is called Navigation System. The most used Navigation Systems, developed for a wide range of vehicles, are Inertial Navigation System (INS) and Global Positioning System (GPS). These two systems have their advantages and disadvantages, which makes them complementary. The best estimates of the aircraft position, velocity and attitude can be obtained by GPS/INS integration employing Kalman filter implementation. Kalman filtering is a form of optimal estimation characterized by recursive evaluation, and an internal model of the dynamics of the system being estimated

융합정보논문지(구 중소기업융합학회논문지), 2018

Recently, UAV has been utilized not only for military usage but also for the whole industry, and studies for utilizing UAV more efficiently are continued[1-5]. When an UAV is flying in the air measuring and controlling the current operating state and flight attitude of the UAV is a very important issue

2005

Part-time studies of the MSc program as source of number of technical and scientific innovations in Automation and Computer Based Systems Engineering at Faculty of Electrical Engineering of SS Cyril and Methodius University in Skopje, are run for more than 15 years. This innovation in Unmanned Aerial Vehicles (UAV's) is the most recent one. By definition, navigation is a process of determining the navigational parameters of the centre of mass, of the moving object. The system that provides us the navigational parameters is called Navigation System. The most used Navigation Systems, developed for a wide range of vehicles, are Inertial Navigation System (INS) and Global Positioning System (GPS). These two systems have their advantages and disadvantages, which makes them complementary. The best estimates of the aircraft position, velocity and attitude can be obtained by GPS/INS integration employing Kalman filter implementation. Kalman filtering is a form of optimal estimation characterized by recursive evaluation, and an internal model of the dynamics of the system being estimated

Aerospace Science and Technology, 2019

Manipulating unmanned aerial vehicles (MUAVs) are aerial robots equipped with a mechanism to physically interact with the environment. State estimation of such robots is a challenging problem due to inherent couplings, nonlinearities and uncertainties of MUAV complex dynamics and, therefore, popular algorithms such as extended Kalman filter may not be applicable. With the above considerations, this paper formulates two variants of the unscented Kalman filter using (i) general and (ii) spherical unscented transform to address state estimation problem in MUAVs. In order to examine the effect of estimation quality on overall control performance, first the coupled dynamics of a quadcopter endowed with a robotic manipulator is presented. Next, a linear-quadratic-Gaussian (LQG) control is designed to achieve simultaneous control of the quadcopter and its manipulator. Then, the performance of each unscented Kalman filter algorithm is compared with that of extended Kalman filter in the context of estimation accuracy, overall control performance, and algorithm execution time. Additionally, sensitivity of the proposed approaches to increasing noise levels and total loss of sensory data are examined.