Paulo Jorge Ramalho Oliveira

A Geometric Approach to Strapdown Magnetometer Calibration in Sensor Frame

Abstract In this work a new algorithm is derived for the onboard calibration of three-axis strapdown magnetometers. The proposed calibration method is written in the sensor frame, and compensates for the combined effect of all linear time-invariant distortions, namely soft iron, hard iron, sensor non-orthogonality, bias, among others. A Maximum Likelihood Estimator (MLE) is formulated to iteratively find the optimal calibration parameters that best fit to the onboard sensor readings, without requiring external attitude references. It is shown that the proposed calibration technique is equivalent to the estimation of an ellipsoidal surface, and that the sensor alignment matrix is given by the solution of the orthogonal Procrustes problem. Good initial conditions for the iterative algorithm are obtained by a suboptimal batch least squares computation. Simulation and experimental results with low-cost sensors data are presented, supporting the application of the algorithm to autonomous vehicles and other robotic platforms.

Robotic ocean vehicles for marine science applications: the European ASIMOV project

The key objective of the ASIMOV project is the development and integration of advanced technological systems to achieve coordinated operation of an Autonomous Surface Craft (ASC) and an Autonomous Underwater Vehicle (AUV) while ensuring a fast communication link between the two vehicles. The ASC/AUV ensemble is being used to study the extent of shallow water hydrothermalism and to determine the patterns of community diversity at vents in the D. Joao de Castro (DJC) bank in the Azores.

Geometric Approach to Strapdown Magnetometer Calibration in Sensor Frame

In this work a new algorithm is derived for the onboard calibration of three-axis strapdown magnetometers. The proposed calibration method is written in the sensor frame, and compensates for the combined effect of all linear time-invariant distortions, namely soft iron, hard iron, sensor nonorthogonality, and bias, among others. A maximum likelihood estimator (MLE) is formulated to iteratively find the optimal calibration parameters that best fit to the onboard sensor readings, without requiring external attitude references. It is shown that the proposed calibration technique is equivalent to the estimation of a rotation, scaling and translation transformation, and that the sensor alignment matrix is given by the solution of the orthogonal Procrustes problem. Good initial conditions for the iterative algorithm are obtained by a suboptimal batch least squares computation. Simulation and experimental results with low-cost sensors data are presented and discussed, supporting the application of the algorithm to autonomous vehicles and other robotic platforms.

Study and implementation of an EKF GIB-based underwater positioning system

The paper addresses the problem of estimating the position of an underwater target in real time. In the scenario adopted, the target carries a pinger that emits acoustic signals periodically, as determined by a very high precision clock that is synchronized with GPS, prior to system deployment. The target is tracked from the surface by using a system of four buoys equipped with hydrophones and electronic circuitry that measures the times of arrival of the acoustic signals emitted by the pinger or, equivalently, the four target-to-buoy range measurements (a commercial version of this setup is the GIB system). Due to the finite speed of propagation of sound in water, these measurements are obtained with different latencies. The paper tackles the problem of underwater target tracking in the framework of extended Kalman filtering by relying on a purely kinematic model of the target. The paper further shows also how the differently delayed measurements can be merged using a back and forward fusion approach. A measurement validation procedure is introduced to deal with dropouts and outliers. Simulation as well as experimental results illustrate the performance of the filter proposed.

Single range aided navigation and source localization: Observability and filter design

This paper addresses the problems of navigation and source localization by mobile agents based on the range to a single source, in addition to relative velocity readings. The contribution of the paper is two-fold: (i) necessary and sufficient conditions on the observability of the nonlinear system are derived, which are useful for trajectory planning and motion control of the agent; and (ii) a nonlinear system, which given the input and output of the system is regarded as linear time-varying, is proposed and a Kalman filter is applied to successfully estimate the system state. Simulation results are presented in the presence of realistic measurement noise that illustrate the performance achieved with the proposed solution.

A nonlinear position and attitude observer on SE(3) using landmark measurements

This paper addresses the problem of position and attitude estimation, based on landmark readings and velocity measurements. A derivation of a nonlinear observer on SE(3) is presented, using a Lyapunov function conveniently expressed as a function of the difference between the estimated and the measured landmark coordinates. The resulting feedback laws are explicit functions of the landmark measurements and velocity readings, exploiting the sensor information directly in the observer. The proposed observer yields almost global asymptotic stabilization of the position and attitude errors and exponential convergence in any closed ball inside the region of attraction. Also, it is shown that the asymptotic convergence of the estimation error trajectories is shaped by the landmark geometry and observer design parameters. The problem of non-ideal velocity readings is also considered, and the observer is augmented to compensate for bias in the angular and linear velocity measurements. The resulting position, attitude, and bias estimation errors are shown to converge exponentially fast to the desired equilibrium points, for bounded initial estimation errors. Simulation results are presented to illustrate the stability and convergence properties of the observer.

Vehicle and Mission Control of the DELFIM Autonomous Surface Craft

DELFIM is an autonomous surface craft developed at ISR/IST for automatic marine data acquisition and to serve as an acoustic relay between submerged craft and a support vessel. The paper describes the navigation, guidance, and control systems of the vehicle, together with the mission control system that allows end-users to seamlessly program and run scientific missions at sea. Practical results obtained during sea tests in the Atlantic, near Azores islands, are briefly summarized and discussed.

A Nonlinear GPS/IMU based observer for rigid body attitude and position estimation

This work proposes a position and attitude nonlinear observer based on inertial measurements and GPS pseudorange readings. The observation problem is formulated on SE(3), and the solution yields exponential convergence of the attitude and position estimates. The GPS pseudorange measurements and inertial sensor readings are exploited directly in the observer, and the integration of vector readings in the observer is discussed. The proposed observer dynamics compensate for the bias in the angular velocity sensor and the clock offset in GPS pseudorange measurements. The stability of the position and velocity estimates in the presence of bounded accelerometer noise is also analyzed. The properties of the GPS/IMU based observer are illustrated in simulation for a rigid body describing a challenging trajectory.

Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

Maneuvering vessel detection and tracking (VDT), incorporated with state estimation and trajectory prediction, are important tasks for vessel navigational systems (VNSs), as well as vessel traffic monitoring and information systems (VTMISs) to improve maritime safety and security in ocean navigation. Although conventional VNSs and VTMISs are equipped with maritime surveillance systems for the same purpose, intelligent capabilities for vessel detection, tracking, state estimation, and navigational trajectory prediction are underdeveloped. Therefore, the integration of intelligent features into VTMISs is proposed in this paper. The first part of this paper is focused on detecting and tracking of a multiple-vessel situation. An artificial neural network (ANN) is proposed as the mechanism for detecting and tracking multiple vessels. In the second part of this paper, vessel state estimation and navigational trajectory prediction of a single-vessel situation are considered. An extended Kalman filter (EKF) is proposed for the estimation of vessel states and further used for the prediction of vessel trajectories. Finally, the proposed VTMIS is simulated, and successful simulation results are presented in this paper.

Sensor-Based Globally Asymptotically Stable Filters for Attitude Estimation: Analysis, Design, and Performance Evaluation

This technical note presents the design, analysis, and performance evaluation of a novel globally asymptotically stable (GAS) filter for attitude estimation. The design is sensor-driven and departs from traditional solutions as no explicit representations of the attitude are considered. The proposed solution yields unique estimates and it does not suffer from drawbacks such as singularities, topological limitations for achieving global stabilization, or unwinding phenomena. The performance of the overall attitude estimation solution is evaluated with the design and implementation of an Attitude and Heading Reference System (AHRS) based on a single low-cost Inertial Measurement Unit. The performance of the proposed AHRS is assessed experimentally using a high precision motion rate table, which provides ground truth signals for comparison with the resulting estimates.