MEMS Attitude Heading Reference Systems are widely employed to determine a system’s attitude, but sensor measurement biases limit their accuracy. This paper introduces a novel factor graph-based method called MAgnetometer and GYroscope Calibration (MAGYC). MAGYC leverages three-axis angular rate measurements from an angular rate gyroscope to enhance calibration for batch and online applications. Our approach imposes less restrictive conditions for instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field or instrument attitude, and facilitates integration into factor graph algorithms within Smoothing and Mapping frameworks. We evaluate the proposed methods through numerical simulations and in-field experimental assessments using a sensor installed on an underwater vehicle. Ultimately, our proposed methods reduced the underwater vehicle’s heading error standard deviation from 6.21° to 0.57° for a standard seafloor mapping survey.

Reducing material spillage by robotic mining mobile manipulators, such as front-end loaders, is necessary to improve mining operations. To this end, the present work proposes an approach to reduce disturbances on the end-effector induced by the terrain and propagated through the wheels and arm links of the machine. The proposed approach is based on an H∞control strategy that includes a feedforward action, computed using the pitch rate of the mobile base, and considers the hydraulic arm dynamics, as well as the reaction forces in the contact points of the mobile base, which is modeled as a floating body with non-permanent ground contacts. Alternative control schemes based on the classic proportional-derivative (PD) control, and the Active Disturbance Rejection Control (ADRC), with and without feedforward action, were also implemented and experimentally evaluated using a semiautonomous Cat®262C compact skid-steer loader equipped with inclination and inertial sensors. The proposed method reduces disturbances by at least 70% when climbing ramps at 25% of the machine's maximum speed, and by at least 20% when driving over speed bumps which produce disturbances similar to that caused by stones. The proposed disturbance attenuation strategy should help reducing the spillage of material when driving over mounds, inclines or spilled rocks, especially considering that even if existing autonomous machines are able to drive with little operator supervision along mining galleries, they are often unable to avoid disturbing material on the ground or the characteristic unevenness of mining terrains.

This paper reports on a 3D photomosaicing pipeline using data collected from an autonomous underwater vehicle performing simultaneous localization and mapping (SLAM). The pipeline projects and blends 2D imaging sonar data onto a large-scale 3D mesh that is either given a priori or derived from SLAM. Compared to other methods that generate a 2D-only mosaic, our approach produces 3D models that are more structurally representative of the environment being surveyed. Additionally, our system leverages recent work in underwater SLAM using sparse point clouds derived from Doppler velocity log range returns to relax the need for a prior model. We show that the method produces reasonably accurate surface reconstruction and blending consistency, with and without the use of a prior mesh. We experimentally evaluate our approach with a Hovering Autonomous Underwater Vehicle (HAUV) performing inspection of a large underwater ship hull.

This paper reports on a novel method for estimating the sensor bias of three-axis magnetometers (or any other field sensor). Our approach employs relative angular position measurements to estimate the three-axis magnetometer measurement bias, significantly improving magnetometer-based attitude estimation. Relative angular position measurements can be calculated from a variety of sources, including multiview image registration or laser-based scan matching. We report two methods implementing this approach based on batch linear least squares and a real-time discrete Kalman filter. Compared with previously reported methods our approach is time independent and less restrictive with data sampling. In addition, our two methods (i) are empirically shown to impose less restrictive conditions for the movements of the instrument required for calibration, (ii) do not require knowledge of the direction of the field (e.g., the local magnetic field) or the attitude of the instrument, and (iii) also ensure convergence for the estimated parameters. The proposed methods are evaluated and compared with previously reported methods in both numerical simulation and in comparative experimental evaluation using cameras and magnetometer sensors under different conditions.

Doppler navigation is one of the most widely practiced navigation methods for underwater robotic vehicles, yet its accuracy is limited both by sensor calibration and by the accuracy of the vehicle's attitude measurement. This paper reports a method to improve the dynamic attitude estimation for underwater vehicles employing Doppler sonar and low-cost micro-electro-mechanical systems (MEMS) attitude and heading reference systems (AHRSs). Our approach employs three-axis velocity measurements from a Doppler velocity log (DVL) to estimate acceleration to provide corrections to the AHRS accelerometer signals that improve the dynamic estimates of vehicle roll and pitch. We report an implementation of this approach with a nonlinear complementary filter to estimate the vehicle attitude dynamically. We report the results of an in-water comparative experimental evaluation of the proposed and previously reported attitude estimation methods using low-cost MEMS AHRSs. We also report an experimental evaluation of the effect of the attitude estimation accuracy on the spatial accuracy of three-dimensional Doppler navigation of underwater vehicles.

We report a comparative performance evaluation, using at-sea field data, of recently reported methods for the problem of in-situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and inertial navigation sensors arising in the navigation of underwater vehicles. Most previously reported solutions to this alignment calibration problem require the use of absolute navigation fixes of the underwater vehicle, thus requiring additional navigation sensors and/or beacons to be located externally and apart from the underwater vehicle. We briefly review four recently reported alignment calibration methods employing only internal vehicle navigation sensors for velocity, acceleration, attitude, and depth. We report the results of comparative analysis of the performance of these recently reported methods and a previously reported method with navigation data from deep-water survey missions of the Sentry autonomous underwater vehicle conducted in March, 2011 in the Kermadec Arc in the Southern Pacific Ocean. The results reveal consistent differences in performance of the various methods when analyzed on navigation data from several different vehicle dives.

This paper reports the results of an in-water laboratory evaluation of the state estimation accuracy of a commercially available inertial navigation system (INS), and the effect of variations in the accuracy and the update rate of INS correction data on the accuracy of the INS state estimate. The navigation accuracy of the INS state estimate is evaluated by comparison with navigation data from a highly accurate navigation system available in our laboratory that employs high-frequency acoustic time-of-flight ranging, precision pressure depth sensing, bottom-lock Doppler sonar, and a true-North-seeking gyrocompass that is integral to the INS.

This paper reports the development and in-water experimental evaluation of two new methods for the problem of in-situ calibration of the alignment rotation matrix between Doppler sonar velocity sensors and gyrocompass attitude sensors arising in the navigation of underwater vehicles. Most previously reported solutions to this alignment calibration problem require the use of absolute navigation fixes of the underwater vehicle, thus requiring additional navigation sensors and/or beacons to be located externally and apart from the underwater vehicle. We report two alignment calibration methods employing only internal vehicle navigation sensors for velocity, acceleration, attitude, and depth. Results from laboratory experiments comparing these methods to previously reported techniques indicate satisfactory performance of the proposed methods.