A Preliminary Study on Attitude Measurement Systems Based on Low Cost Sensors

Abstract

The increasingly use of Autonomous Underwater Vehicles (AUVs) in several context led to a rapid development and enhancement of their technologies, allowing the automatization of many tasks. One of the most challenging tasks of AUVs still remains their robust positioning and navigation, since classical global positioning techniques are generally not available for their operations. Inertial Navigation System (INS) methods provide the vehicle current position and orientation integrating data acquired by the internal accelerometer and gyroscope. This system has the advantage of not needing to either send or receive signals from other systems; however, among the errors the sensors are mainly affected by, the most critical one is related to their drift, which makes the position error growing over time. The attenuation of the effect of these problematics is generally achieved combining different positioning methods, as for example acoustic-or geophysical-based ones. An accurate estimation of the device orientation is anyway necessary to get satisfying results in terms of position and autonomous navigation. In this paper, a preliminary study on the use of smartphone low-cost sensors to perform attitude estimation is presented. With the final aim of developing a cheaper and more accessible underwater positioning system, a first analysis is conducted to verify the accuracy of the attitude angles obtained by the integration of smartphone data acquired in different operative settings. Different filtering methods will be employed.

DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea

“…The same filter has been used in [22], which developed a novel navigation system for autonomous underwater vehicles that works without the presence of a GPS device, not available in underwater scenarios. Researchers in [23] proposed an Adaptive Kalman Filter which is able to provide pose estimations based on low-cost AHRS devices, whereas [24] and [25] investigated the use of AHRS in smartphones as cheap but reliable devices for angles estimation. A novel error-state Kalman filter is presented in [26], which provides highly accurate IMU orientation estimates which result to be robust to fluctuations in the registered local magnetic field or caused by abrupt movements.…”

DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

“…Nowadays, small-scale robots usually mount more affordable sensor systems (e.g., AHRS), which greatly benefit from the power of KF and can equally provide high precision and reliable results. An example is the work of the authors of [ 10 ], who proposed an effective adaptive Kalman filter, which is able to exploit low-cost AHRS for efficient attitude estimation under various dynamic conditions, and of [ 11 ], who evaluated orientation estimation performances of smartphones in different settings.…”

“…The use of KF in robotic applications is ubiquitous. [4] developed a GPS/IMU multisensor fusion algorithm to increase the reliability of the position information, while [5] and [6] proposed an effective Kalman Filter which is able to exploit low-cost AHRS for efficient attitude estimation under various dynamic conditions, with an interesting underwater application developed by [7] and [8].…”