A main problem in autonomous vehicles in general, and in UAV in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an AHRS based on the UKF using the TRIAD algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the EKF . The paper presents field experiment results using a real fixed-wing UAV . The results show good real-time performance with low computational cost in a microcontroller.
The use of IMU based in MEMS technology to estimate the attitude angles in the industry has been increasing in the recent years, like a fastening tool tracking system in @cite_21 .
@cite_21
2016599477 1775300292 1996213997 1976577079
In this paper, a low-cost attitude estimation system is introduced. The system is developed with MEMS sensors including rate gyros and accelerometers. Composition and principle of the system are described. The rigid body kinematics is modeled with quaternion, such that eliminates attitude estimation singularities. The real-time Kalman filter is designed. Experiments were conducted in both static and dynamic conditions. The experimental results demonstrate that the algorithm and hardware are feasible and suitable for the application where critical accuracy and real-time requirements are needed. It this paper we present a novel minimal case solution to the calibrated relative pose problemusing 3 point correspondences for the case of two known orientation angles. This case is relevant when a camera is coupled with an inertial measurement unit (IMU) and it recently gained importance with the omnipresence of Smartphones (iPhone, Nokia N900) that are equippedwith accelerometers tomeasure the gravity normal. Similar to the 5-point (6-point), 7-point, and 8-point algorithm for computing the essential matrix in the unconstrained case, we derive a 3-point, 4-point and, 5-point algorithm for the special case of two known orientation angles. We investigate degenerate conditions and show that the new 3-point algorithm can cope with planes and even collinear points. We will show a detailed analysis and comparison on synthetic data and present results on cell phone images. As an additional application we demonstrate the algorithm on relative pose estimation for a micro aerial vehicle's (MAV) camera-IMU system. Camera tracking system for augmented reality applications that can operate both indoors and outdoors is described. The system uses a monocular camera, a MEMS-type inertial measurement unit (IMU) with 3-axis gyroscopes and accelerometers, and GPS unit to accurately and robustly track the camera motion in 6 degrees of freedom (with correct scale) in arbitrary indoor or outdoor scenes. IMU and camera fusion is performed in a tightly coupled manner by an error-state extended Kalman filter (EKF) such that each visually tracked feature contributes as an individual measurement as opposed to the more traditional approaches where camera pose estimates are first extracted by means of feature tracking and then used as measurement updates in a filter framework. Robustness in feature tracking and hence in visual measurement generation is achieved by IMU aided feature matching and a two-point relative pose estimation method, to remove outliers from the raw feature point matches. Landmark matching to contain long-term drift in orientation via on the fly user generated geo-tiepoint mechanism is described. This paper concerns with the evaluation of methods for the estimation of both temporal and spatial visual attention using a head-worn inertial measurement unit (IMU). Aimed at tasks where there is a wearer-object interaction, we estimate the when and the where the wearer is interested in. We evaluate various methods on a new egocentric dataset from 8 volunteers and compare our results with those achievable with a commercial gaze tracker used as ground-truth. Our approach is primarily geared for sensor-minimal EyeWear computing.