Orientation Estimation by Means of Extended Kalman Filter, Quaternions, and Charts

Bernal-Polo, Pablo; Barberá, Humberto Martínez

doi:10.14198/jopha.2017.8.1.03

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The position and attitude of a body in 3D space can be defined by the three transnational and the three rotational coordinates, which relate the origin and orientation of the body-fixed coordinate system to the world frame. In particular, the orientation of a rigid body is usually expressed by a transformation matrix, the elements of which are generally parameterized in terms of Euler angles, rotation vectors, rotation matrices, and unit quaternions [ 36 ]. A detailed survey of this representation can be found in [ 37 ].…”

Section: Theoretical Notions and Methodsmentioning

confidence: 99%

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

Russo

Ciaccio

Troisi

2021

Sensors

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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 configured, 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.

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Section: Theoretical Notions and Methodsmentioning

confidence: 99%

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

Russo

Ciaccio

Troisi

2021

Sensors

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“…The orientation definition for a rigid body is generally made through a transformation matrix containing a parametrization of the Euler angles, unit quaternions, rotation vectors or rotation matrices [61]. Among them, the Euler angles allow for a more intuitive analysis in the 3D space and can be defined as follows:…”

Section: A Orientation Estimation Overviewmentioning

confidence: 99%

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

2022

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The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES, a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method as a valid support to visual-based odometry techniques.

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“…The orientation of a rigid body is usually expressed by a transformation matrix in which the elements are generally parameterized in terms of Euler angles, rotation vectors, rotation matrices, and unit quaternions [59]. The Euler angles are the most intuitive expression as they allow a simple analysis of the body orientation in the 3D space.…”

Section: Orientation Estimation Overviewmentioning

confidence: 99%

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

Ciaccio¹,

Russo²,

Troisi³

2021

Preprint

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The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

show abstract

Orientation Estimation by Means of Extended Kalman Filter, Quaternions, and Charts

Cited by 5 publications

References 4 publications

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

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

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

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

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