Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

Noureldin, Aboelmagd; Karamat, Tashfeen B.; Georgy, Jacques

doi:10.1007/978-3-642-30466-8

Cited by 403 publications

(408 citation statements)

References 1 publication

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“…A KF comprises five equations which operate in a sequential manner. The flow of algorithm is based on two states: prediction state and correction state (Noureldin et al, 2013). The idea is depicted in figure 4.…”

Section: Concept Of Kalman Filtermentioning

confidence: 99%

Concept of AHRS Algorithm Designed for Platform Independent Imu Attitude Alignment

Tomaszewski

Rapiński

Pelc‐Mieczkowska

2017

Reports on Geodesy and Geoinformatics

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Nowadays, along with the advancement of technology one can notice the rapid development of various types of navigation systems. So far the most popular satellite navigation, is now supported by positioning results calculated with use of other measurement system. The method and manner of integration will depend directly on the destination of system being developed. To increase the frequency of readings and improve the operation of outdoor navigation systems, one will support satellite navigation systems (GPS, GLONASS ect.) with inertial navigation. Such method of navigation consists of several steps. The first stage is the determination of initial orientation of inertial measurement unit, called INS alignment. During this process, on the basis of acceleration and the angular velocity readings, values of Euler angles (pitch, roll, yaw) are calculated allowing for unambiguous orientation of the sensor coordinate system relative to external coordinate system. The following study presents the concept of AHRS (Attitude and heading reference system) algorithm, allowing to define the Euler angles.The study were conducted with the use of readings from low-cost MEMS cell phone sensors. Subsequently the results of the study were analyzed to determine the accuracy of featured algorithm. On the basis of performed experiments the legitimacy of developed algorithm was stated.

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Section: Concept Of Kalman Filtermentioning

confidence: 99%

Concept of AHRS Algorithm Designed for Platform Independent Imu Attitude Alignment

Tomaszewski

Rapiński

Pelc‐Mieczkowska

2017

Reports on Geodesy and Geoinformatics

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“…By completion of this task, KF provides optimal least mean variance estimation of searched states (Kalman 1960;Welch, Bishop 2006). In case of INS/GPS integration there are usually errors of values calculated by the INS system (Solimeno 2007;Noureldin et al 2009Noureldin et al , 2012Zhao 2011;Groves 2013;Grewal et al 2013). Kalman filtering also provides information considering accuracy of performed calculations to determine the impact of each observable on the final result.…”

Section: Basic Concept Of Kalman Filteringmentioning

confidence: 99%

“…Use of INS allows to increase frequency, availability and accuracy of positioning results. Such combined systems are called INS/GPS integrated navigation (Groves 2013;Noureldin et al 2012;Grewal et al 2013). Expensive INS are used for many years in marine, aviation and missile navigation.…”

Section: Introductionmentioning

confidence: 99%

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Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

Tomaszewski

2017

Proccedings of 10th International Conference "Environmental Engineering"

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Abstract. Nowadays, along with the advancement of technology one can notice the rapid development of various types of navigation systems. So far, the most popular satellite navigation, is now supported by positioning results calculated with the use of other measurement system. The method and manner of integration will depend directly on the destination of developed system. To increase the frequency of readings and improve the operation of outdoor navigation systems, one can support satellite navigation systems (GPS, GLONASS ect.) with inertial navigation (INS/GPS systems). For the last two decades, due to the miniaturization in electronics it is possible to build GPS/INS systems with the use of low cost MEMS devices. The following study presents the concept of Kalman filter based integration algorithm designed for cheap micro -electronic navigation platform. The tests of considered algorithm were made with the use of MEMS smartphone sensors. Subsequently the results of the study were analyzed to determine the accuracy of featured algorithm.

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“…One of the challenges indoor navigation confronts is the absence of GPS signals in the indoor environment (Misra and Enge, 2001;Noureldin et al, 2012). To handle this issue, alternative techniques are introduced to obtain a satisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

A LiDAR-Aided Indoor Navigation System for UGVs

et al. 2014

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Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy. K E Y

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Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

Cited by 403 publications

References 1 publication

Concept of AHRS Algorithm Designed for Platform Independent Imu Attitude Alignment

Concept of AHRS Algorithm Designed for Platform Independent Imu Attitude Alignment

Concept of INS/GPS Integration Algorithm Designed for MEMS Based Navigation Platform

A LiDAR-Aided Indoor Navigation System for UGVs

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