Model Validation of an Open-source Framework for Post-processing INS/GNSS Systems

González, Rodrigo A.; Catania, Carlos; Dabove, Paolo; Taffernaberry, Juan Carlos; Piras, Marco

doi:10.5220/0006313902010208

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Raw experimental data were acquired from a Micro‐Electro Mechanical System‐Strapdown Inertial Navigation System (MEMS‐SINS) (Ekinox‐D Inertial Navigation System) with sampling frequency 200 Hz. The characteristics of gyroscopes and accelerometers of this SINS were obtained in (Gonzalez & Dabove, 2019; Gonzalez et al., 2017) using Allan variance method. A Global Navigation Satellite System/Global Positioning System (GLONASS/GPS) receiver was used with sampling frequency of 5 Hz.…”

Section: Resultsmentioning

confidence: 99%

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A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar

Gavrilov

2021

Navigation

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Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX‐based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag‐space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX‐aided UKF outperforms other methods that use different input configurations for neural networks.

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Section: Resultsmentioning

confidence: 99%

“…Both systems were mounted on the roof of the vehicle, as shown in Figure 8a. The experiment was conducted in the city of Turin in Italy (Gonzalez et al., 2017). The duration of the dataset used in this work is 2,300 s. The trajectory of the vehicle is shown in Figure 8b.…”

Section: Resultsmentioning

confidence: 99%

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

Bitar

Gavrilov

2021

Navigation

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“…An online trajectory generator has been used in [22] as a stage between image processing algorithms and robot motion controllers for controlling robot arms. An IMU simulation framework, NaveGo, has been introduced in [23] and evaluated in [24]. The work provides a comprehensive mathematical model and is corroborated by a freely distributable implementation in MATLAB.…”

Section: Related Workmentioning

confidence: 99%

A Potrace-based Tracing Algorithm for Prescribing Two-dimensional Trajectories in Inertial Sensors Simulation Software

Ціж¹,

Biotechnologies²,

Marusenkova³

2021

IJMECS

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Inertial measurement units based on microelectromechanical systems are perspectives for motion capture applications due to their numerous advantages. A motion trajectory is restored using a well-known navigation algorithm, which assumes integration of the signals from accelerometers and gyroscopes. Readings of both sensors contain errors, which quickly accumulate due to integration. The applicability of an inertial measurement unit for motion capture depends on the trajectory being tracked and can be predicted due to the simulation of signals from inertial sensors. The first simulation step is prescribing a motion trajectory and corresponding velocities. The existing simulation software provides no user-friendly graphical tools for the completion of this step. This work introduces an algorithm for the simulation of accelerometer signals upon a two-dimensional trajectory drawn with a computer mouse and then vectorized. We propose a modification of the Potrace algorithm for tracing motion trajectories. Thus, a trajectory and velocities can be set simultaneously. The obtained results form a basis for simulating three-dimensional motion trajectories since the latter can be represented by three mutually orthogonal two-dimensional projections.

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“…The AV analysis is carried out by using the tools provided by NaveGo [14,15,16], which is an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and performing inertial sensors analysis. Table 2 exhibits the results of applying the AV procedure on the static measurements.…”

Section: Static Analysismentioning

confidence: 99%

Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

González¹,

Dabove

2019

Sensors

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Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

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Model Validation of an Open-source Framework for Post-processing INS/GNSS Systems

Cited by 14 publications

References 6 publications

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

A novel approach for aiding unscented Kalman filter for bridging GNSS outages in integrated navigation systems

A Potrace-based Tracing Algorithm for Prescribing Two-dimensional Trajectories in Inertial Sensors Simulation Software

Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

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