Uncertainty Modeling for AUV Acquired Bathymetry

Abstract Autonomous Underwater Vehicles (AUVs) are used across a wide range of mission scenarios and from an increasingly diverse set of operators. Use of AUVs for shallow water (less than 200 meters) mapping applications is of increasing interest. However, an update of the total propagated uncertainty TPU model is required to properly attribute bathymetry data acquired from an AUV platform compared with surface platform acquired data. An overview of the parameters that should be considered for data acquired from an AUV platform is discussed. Data acquired in August 2014 using NOAA’s Remote Environmental Measuring UnitS (REMUS) 600 AUV in the vicinity of Portsmouth, NH were processed and analyzed through Leidos’ Survey Analysis and Area Based EditoR (SABER) software. Variability in depth and position of seafloor features observed multiple times from repeat passes of the AUV, and junctioning of the AUV acquired bathymetry with bathymetry acquired from a surface platform are used to evaluate the TPU model and to characterize the AUV acquired data

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de Málag

Danae++: A smart approach for denoising underwater attitude estimation

One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely con-figured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++ is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks

Velocity-aided Attitude Estimation for Accelerated Rigid Bodies

Two nonlinear observers for velocity-aided attitude estimation, relying on gyrometers, accelerometers, magnetometers, and velocity measured in the body-fixed frame, are proposed. As opposed to state-of-the-art body-fixed velocity-aided attitude observers endowed with local properties, both observers are (almost) globally asymptotically stable, with very simple and flexible tuning. Moreover, the roll and pitch estimates are globally decoupled from magnetometer measurements

Online Inertial Measurement Unit Sensor Bias And Attitude Estimation For The Calibration And Improved Performance Of Attitude And Heading Reference Systems

Dynamic instrumentation and estimation of vehicle attitude is critical to the accurate navigation of land, sea, and air vehicles in dynamic motion. The focus of this thesis is the development of algorithms for improved performance of attitude and heading reference systems (AHRSs) and robotic vehicle navigation. inertial measurement unit (IMU) sensor bias estimation methods for use in the calibration of AHRSs and an adaptive attitude estimator operating directly of SO(3) are reported. The reported algorithms provide online calibration and attitude estimation methods which enable more accurate navigation for robotic vehicles. This thesis differentiates AHRSs into two categories – AHRSs that estimate true-North heading and those that estimate magnetic north heading. Chapters 3-5 report several novel algorithms for micro-electro-mechanical systems (MEMS) IMU sensor bias estimation. Observability, stability, and parameter convergence are evaluated in numerical simulations, full-scale vehicle laboratory experiments, and full-scale field trials in the Chesapeake Bay, MD. Chapter 6 reports an adaptive sensor bias observer and attitude observer operating directly on SO(3) for true-North gyrocompass systems that utilize six-degrees of freedom (DOF) IMUs with three-axis accelerometers and three-axis angular rate gyroscopes (without magnetometers) to dynamically estimate the instrument’s time-varying true-North attitude (roll, pitch, and geodetic heading) in real-time while the instrument is subject to a priori unknown rotations. Stability proofs for the reported bias and attitude observers, preliminary simulations, and a full-scale vehicle trial are reported. The presented calibration methods are shown experimentally to improve calibration of AHRS attitude estimation over current state of the art sensor bias estimation methods, and this thesis presents a true-North gyrocompass system based on adaptive observers for use with strap-down IMUs. These results may prove to be useful in the development of navigation systems for small low-cost robotic vehicles