Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System

This paper describes a robust and simple algorithm for an attitude and heading reference system (AHRS) based on low-cost MEMS inertial and magnetic sensors. The proposed approach relies on a gain-scheduled complementary filter, augmented by an acceleration-based switching architecture to yield robust performance, even when the vehicle is subject to strong accelerations. Experimental results are provided for a road captive test during which the vehicle dynamics are in high-acceleration mode and the performance of the proposed filter is evaluated against the output from a conventional linear complementary filter

DEVELOPMENT OF A LOW-COST IMU FOR SWIMMERS’ EVALUATION

Swimmers improvement is built on rigorous and controlled training. This work aimed to develop a light-weight, practical and low-cost inertial measurement unit (IMU) for swimming monitoring. A 21g device was optimized for including the data acquisition hardware (based on an attitude and heading reference system – AHRS). Eleven male swimmers with different skill levels tested the system under the four different swimming strokes. Based on the AHRS data, three novel indicators were developed: trunk elevation, body balance and body rotation; and anayzed on an individual basis. The proposed unit has unique features to be further explored for training monitoring

Pose identification and updating in autonomous vehicles

In this paper, a novel algorithm to know the pose of any autonomous vehicle is described. Such a system (Attitude and Heading Reference System, AHRS) is essential for real time vehicle navigation, guidance and control applications. For low funded projects, with simple sensors, efficient and robust algorithms become necessary for an acceptable performance, and the well-known extended Kalman filter (EKF) fulfills those requirements. In this kind of applications, the use of the EKF in direct configuration has been much less explored than its counterpart, the EKF in indirect configuration. Specifically, in this paper a novel method based on an Extended Kalman Filter in direct configuration is proposed, where the filter is explicitly derived from both kinematic and errors models. Experiments with real data show that the proposed method is able to maintain an accurate and drift-free attitude and heading estimation.Peer ReviewedPostprint (published version

A practical method for implementing an attitude and heading reference system

This paper describes a practical and reliable algorithm for implementing an Attitude and Heading Reference System (AHRS). This kind of system is essential for real time vehicle navigation, guidance and control applications. When low cost sensors are used, efficient and robust algorithms are required for performance to be acceptable. The proposed method is based on an Extended Kalman Filter (EKF) in a direct configuration. In this case, the filter is explicitly derived from both the kinematic and rror models. The selection of this kind of EKF configuration can help in ensuring a tight integration of the method for its use in filter-based localization and mapping systems in autonomous vehicles. Experiments with real data show that the proposed method is able to maintain an accurate and drift-free attitude and heading estimation. An additional result is to show that there is no ostensible reason for preferring that the filter have an indirect configuration over a direct configuration for implementing an AHRS system.Postprint (published version

Design and Development of Aerial Robotic Systems for Sampling Operations in Industrial Environment

This chapter describes the development of an autonomous fluid sampling system for outdoor facilities, and the localization solution to be used. The automated sampling system will be based on collaborative robotics, with a team of a UAV and a UGV platform travelling through a plant to collect water samples. The architecture of the system is described, as well as the hardware present in the UAV and the different software frameworks used. A visual simultaneous localization and mapping (SLAM) technique is proposed to deal with the localization problem, based on authors’ previous works, including several innovations: a new method to initialize the scale using unreliable global positioning system (GPS) measurements, integration of attitude and heading reference system (AHRS) measurements into the recursive state estimation, and a new technique to track features during the delayed feature initialization process. These procedures greatly enhance the robustness and usability of the SLAM technique as they remove the requirement of assisted scale initialization, and they reduce the computational effort to initialize features. To conclude, results from experiments performed with simulated data and real data captured with a prototype UAV are presented and discussed

Non-iterative RGB-D-inertial Odometry

This paper presents a non-iterative solution to RGB-D-inertial odometry system. Traditional odometry methods resort to iterative algorithms which are usually computationally expensive or require well-designed initialization. To overcome this problem, this paper proposes to combine a non-iterative front-end (odometry) with an iterative back-end (loop closure) for the RGB-D-inertial SLAM system. The main contribution lies in the novel non-iterative front-end, which leverages on inertial fusion and kernel cross-correlators (KCC) to match point clouds in frequency domain. Dominated by the fast Fourier transform (FFT), our method is only of complexity $\mathcal{O}(n\log{n})$, where $n$ is the number of points. Map fusion is conducted by element-wise operations, so that both time and space complexity are further reduced. Extensive experiments show that, due to the lightweight of the proposed front-end, the framework is able to run at a much faster speed yet still with comparable accuracy with the state-of-the-arts

System Design and Controller Interface of Spacecraft Reaction Wheels

System configuration and design for a three-axis reaction wheel array and corresponding controller for adjusting the rotation of a spacecraft

PC-based aviation training devices (PCATDs): research, development and certification

This paper examines the development of two PCATD’s (one helicopter, one fixed-wing) and their eventual certification by CAA. Certification has demonstrated the potential these devices have for aviation training in New Zealand. Traditionally FTD‘s and PCATD’s have been sourced from foreign companies, and they represent a considerable financial investment for large flying training organisations. The procurement of these simulator types is generally beyond the financial resources of most small to medium sized flying schools. Aviation training in NZ is facing significant financial constraints as well as an increasing demand to simulate complex glass cockpit systems that are now installed in most new General Aviation (GA) aircraft. The development, utilisation and certification of this type of PCATD technology could solve these difficult challenges

Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation

The purpose of navigation is to determine the position, velocity, and orientation of manned and autonomous platforms, humans, and animals. Obtaining accurate navigation commonly requires fusion between several sensors, such as inertial sensors and global navigation satellite systems, in a model-based, nonlinear estimation framework. Recently, data-driven approaches applied in various fields show state-of-the-art performance, compared to model-based methods. In this paper we review multidisciplinary, data-driven based navigation algorithms developed and experimentally proven at the Autonomous Navigation and Sensor Fusion Lab (ANSFL) including algorithms suitable for human and animal applications, varied autonomous platforms, and multi-purpose navigation and fusion approachesComment: 22 pages, 13 figure

A FAULT TOLERANT, DATA FUSION SYSTEM FOR NAVIGATION APPLICATIONS TO A DUCTED FAN VTOL UAV

A Fault Tolerant, Data Fusion (FTDF) algorithm for a Ducted Fan Unmanned Aerial Vehicle (DFUAV) Navigation System is presented. The algorithm have two parts: Gradient Descent (GD) for the Attitude and Heading Reference System (AHRS) and an Interacting Multiple Model (IMM) for position estimation. The GD methodology was designed to fuse the gyroscope, accelerometer, and geomagnetic sensors. The IMM algorithm is able to identify and compensate for multiple sensors data failures. There are three parts in the presentation. Firstly, system identification and the Allan Variance method is used to build dynamic models and noise models for multiple Sensors and Actuators. Secondly, a GD filter is developed for application to the Inertial Measurement Unit (IMU) consisting of tri-axis gyroscopes, accelerometers and magnetometers. The GD filter implementation incorporates magnetic distortion and gyroscope bias drift compensation. The filter uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimized algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. . Finally, the IMM algorithm is used to combine data from multiple sensors simultaneously. This filter uses multiple models that incorporate sensor failures. The probabilities of these models being correct is generated by the IMM. These probabilities can be used to identify sensor failures and compensate for these failures