GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects

Caron, François; Duflos, Emmanuel; Pomorski, Denis; Vanheeghe, Philippe

doi:10.1016/j.inffus.2004.07.002

Cited by 338 publications

(190 citation statements)

References 8 publications

Supporting

Mentioning

186

Contrasting

Unclassified

Order By: Relevance

“…The state is composed by position p referred to the inertial frame, velocity v and acceleration a referred to the body frame, quaternion q and angular velocity w. Moreover, the state contains the bias of accelerometer The position dynamic [26] is modeled by the equation of the uniformly accelerated motion: dynamic matrix. The measures are the acceleration, angular velocity from the IMU, the quaternion's calculated from the accelerometer and magnetometer inside the IMU and the position obtained from a GPS/USBL system or a vision system.…”

Section: Modelmentioning

confidence: 99%

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

Panebianco

Scaradozzi

Ciuccoli

et al. 2017

IRATJ

View full text Add to dashboard Cite

In order to obtain robust and measurable data from the marine environment, citizen science projects need user-friendly tools that, during dives, autonomously gather information in an easy, low-cost and integrated way. Data collected from different devices and by different untrained divers must have good estimation of the position where the information has been acquired. With this goal, in the context of the Green Bubbles project, a novel platform, called DocuScooter has been designed and developed. DocuScooter implements all the algorithms to filter and merge data with correct timestamp and position preparing the complete mission report. After the mission, the diver can upload the report on an appropriate web service to produce a 3D documentation. In order to obtain the underwater position during the dive, a tailored device compatible with the platform is also presented. Result of the prototype of the platform and the first results of the position estimation algorithm are presented and discussed.

show abstract

Section: Modelmentioning

confidence: 99%

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

Panebianco

Scaradozzi

Ciuccoli

et al. 2017

IRATJ

View full text Add to dashboard Cite

show abstract

“…In the same line, Refs. [58,59] present applications using fuzzy system for GPS data classification based on the signal and geometry information with fuzzy reasoning to properly weigh the observations in the Kalman filter. …”

Section: Sensor Characterizationmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

View full text Add to dashboard Cite

This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of ''context''. It shows how its fortune in the distributed computing world eventually per-meated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploita-tion dynamics and architectural aspects peculiar to the fusion domain are presented and discussed.

show abstract

“…In land vehicle applications, Caron et al [11] and Noureldin et al [12] propose machine learning techniques like neural networks, which introduce context variables and errors modelling for each sensor. Authors conclude that with an adequate modelling an accuracy…”

Section: Introductionmentioning

confidence: 99%

Step Characterization using Sensor Information Fusion and Machine Learning

Anacleto¹,

Figueiredo²,

Almeida³

et al. 2015

IJIMAI

View full text Add to dashboard Cite

-A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, lowcost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person's body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

show abstract

GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects

Cited by 338 publications

References 8 publications

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

Context-based Information Fusion: A survey and discussion

Step Characterization using Sensor Information Fusion and Machine Learning

Contact Info

Product

Resources

About