NaveGo: an open-source MATLAB/GNU-Octave toolbox for processing integrated navigation systems and performing inertial sensors profiling analysis.
NaveGo is an open-source MATLAB/GNU Octave toolbox for processing integrated navigation systems and simulating inertial sensors and a GNSS receiver. It also performs inertial sensors analysis using the Allan variance. It is freely available online. It is developed under MATLAB/GNU-Octave due to this programming language has become a de facto standard for simulation and mathematical computing.
NaveGo's motto is "to bring integrated navigation to the masses".
NaveGo is supported at the moment by three academic research groups: GridTics at the National University of Technology (Argentina), Engineering School at the National University of Cuyo (Argentina), and DIATI at the Politecnico di Torino (Italy).
Main features of NaveGo are:
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Processing of an inertial navigation system (INS).
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Processing of a loosely-coupled integrated navigation system (INS/GNSS).
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Simulation of inertial sensors and GNSS.
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Zero Velocity Update (ZUPT) detection algorithm.
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Allan variance technique to characterize inertial sensors' both deterministic and stochastic errors.
The underlying mathematical model of NaveGo is based on two articles:
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R. Gonzalez, J.I. Giribet, and H.D. Patiño. NaveGo: a simulation framework for low-cost integrated navigation systems, Journal of Control Engineering and Applied Informatics, vol. 17, issue 2, pp. 110-120, 2015. Download.
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R. Gonzalez, J.I. Giribet, and H.D. Patiño. An approach to benchmarking of loosely coupled low-cost navigation systems. Mathematical and Computer Modelling of Dynamical Systems, vol. 21, issue 3, pp. 272-287, 2015. Download.
Be aware that the original Kalman filter state vector has been reduced from 21 to 15 states.
NaveGo has been validated by processing real-world data from a real trajectory and contrasting results against Inertial Explorer, a commercial, closed-source software package. Difference between both solutions have shown to be negligible. For more information read the following paper,
- R. Gonzalez, C.A. Catania, P. Dabove, J.C. Taffernaberry, and M. Piras. Model validation of an open-source framework for post-processing INS/GNSS systems. III International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017). Porto, Portugal. April 2017. Download.
Future features of NaveGo will be:
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Tightly-coupled INS/GNSS.
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RTS smoother.
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Adaptive Kalman filter.
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Unscented Kalman filter.
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KML file generator.
If you are using NaveGo in your research, we gently ask you to add the following two cites to your future papers:
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R. Gonzalez, C.A. Catania, P. Dabove, J.C. Taffernaberry, and M. Piras. Model validation of an open-source framework for post-processing INS/GNSS systems. III International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017). Porto, Portugal. April 2017. Download.
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R. Gonzalez, J.I. Giribet, and H.D. Patiño. NaveGo: a simulation framework for low-cost integrated navigation systems, Journal of Control Engineering and Applied Informatics, vol. 17, issue 2, pp. 110-120, 2015. Download.
An URL to NaveGo should be provided as the following cite:
R. Gonzalez, C. Catania, and P. Dabove. NaveGo: An Open-Source MATLAB/GNU-Octave Toolbox for Processing Integrated Navigation Systems and Performing Inertial Sensors Profiling Analysis. Version 1.2. URL: https://github.com/rodralez/NaveGo. DOI: 10.5281/zenodo.2536950. January 2019.
We are looking for contributors to NaveGo! Since integrated navigation is a topic used in several fields as Geomatics, Geology, Mobile Mapping, Autonomous Driving, and even Veterinary (yes, Veterinary!) for animal tracking, we hope other communities other than the navigation community compromise and contribute to this open-source project.
You can contribute in many ways:
- Writing code.
- Writing a manual.
- Reporting bugs.
- Suggesting new features.
If you are interested in joining to NaveGo, please feel free to contact Dr. Rodrigo Gonzalez at rodralez [at] frm [dot] utn [dot] edu [dot] ar.
The following authors have expressed the use of NaveGo within their research:
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R. Rabiee, X. Zhong, Y. Yan and W.P. Tay, "LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2944-2961, August 2019. doi: 10.1109/TITS.2018.2870048. Link. NaveGo is used as a benchmark to compare to a proposed fusion algorithm based on a particle filter to achieve lane-level tracking accuracy under a GNSS-denied environment.
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O. Tokluoğlu and E. Çavuş, "Study of Utilizing Multiple IMUs for Inertial Navigation Systems Without GPS Aid," 2019 1st Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkey, June 2019, pp. 86-89. doi: 10.1109/GPECOM.2019.8778612. Link. The purpose of NaveGo in this work is to test the performance of an INS/GNSS system with multiple IMUs.
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Bac Nghia Vu, Khanh Nhu Nguyen and Mung Huy Vu, "Practical Considerations of IMU Data Generator," 2019 3rd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom), Hanoi, Vietnam, March 2019, pp. 63-68. doi: 10.1109/SIGTELCOM.2019.8696196. Link. NaveGo is used to simulate gyros data. Then, these data is compared to the output of a proposed method for the same goal.
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Mohamed Atia. "Design and simulation of sensor fusion using symbolic engines." Mathematical and Computer Modelling of Dynamical Systems 25.1 (2019): 40-62. February 2019. Link. This work proposes a simulation framework for inertial sensors and an Attitude and Heading Reference System (AHRS). Atia uses as true data input to simulate sensors the same true data that NaveGo provides (see Fig. 7). Unfortunately, Atias' simulator and NaveGo performances are not compared in this work.
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Ren, X., Sun, M., Jiang, C., Liu, L., & Huang, W. (2018). An Augmented Reality Geo-Registration Method for Ground Target Localization from a Low-Cost UAV Platform. Sensors, October 2018, vol. 18, no 11, p. 3739. Link. NaveGo is used to process RTK GPS data in the context of an INS/GNSS system for geo-registration and target localization.
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M. Pachwicewicz and J. Weremczuk, "Accuracy Estimation of the Sounding Rocket Navigation System," 2018 XV International Scientific Conference on Optoelectronic and Electronic Sensors (COE), Warsaw, June 2018, pp. 1-4. doi: 10.1109/COE.2018.8435180. Link. In this paper NaveGo is used as a simulation framework for three types of IMUs.
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M.G. Deepika and A. Arun, "Analysis of INS Parameters and Error Reduction by Integrating GPS and INS Signals," 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, April 2018, pp. 18-23. doi: 10.1109/ICDI3C.2018.00013. Link. This work is completely based on the synthetic data example provided by NaveGo. It is not clear what is the contribution of this paper.
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P.K. Diamantidis, ‘Attitude Navigation using a Sigma-Point Kalman Filter in an Error State Formulation’, Dissertation for Master degree. Department of Space and Plasma Physics, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. 2017. Link. A 30-minutes static measurement of a gyroscope was made with and its Allan Variance plot is presented by using the NaveGo functions.
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Shaoxing Hu, Shike Xu 1, Duhu Wang and Aiwu Zhang. Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems. Sensors 2015, 15(11), 28402-28420. Link. The mathematical model of this work is base on NaveGo's proposed mathematical model.
The example folder contains several types of examples.
This example can be analyzed just executing the file navego_example_allan.m. Firstly, Allan variance is applied to 2-hours of real static measurements from a Sensonor STIM300 IMU. Then, almost 5 hours of synthetic inertial data are created and Allan variance is run on these simulated data.
Two examples of how to use NaveGo to post-process real data are provided as navego_example_real_xxxx.m, one for Ekinox-D IMU and another for MPU-6000 IMU. Both IMUs are integrated with Ekinox-D GNSS. These datasets were generated by driving a vehicle through the streets of Turin city (Italy).
These two real examples are part of the experiments of the following article,
- R. Gonzalez and P. Dabove. Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation. Sensors 2019, 19(18), 3865; https://doi.org/10.3390/s19183865. September 2019. Download.
The file navego_example_synth.m tries to expose how NaveGo can be used step by step. It compares the performances of two simulated INS/GNSS systems, one using an ADIS16405 IMU and another using an ADIS16488 IMU, both fused using a simulated GNSS sensor.
Next, a description of this file is provided.
clc
close all
clear
matlabrc
addpath ../../
addpath ../../simulation/
addpath ../../conversions/
versionstr = 'NaveGo, release v1.2';
fprintf('\n%s.\n', versionstr)
fprintf('\nNaveGo: starting simulation ... \n')
% Comment any of the following parameters in order to NOT execute a particular portion of code
GNSS_DATA = 'ON'; % Generate synthetic GNSS data
IMU1_DATA = 'ON'; % Generate synthetic ADIS16405 IMU data
IMU2_DATA = 'ON'; % Generate synthetic ADIS16488 IMU data
IMU1_INS = 'ON'; % Execute INS/GNSS integration for ADIS16405 IMU
IMU2_INS = 'ON'; % Execute INS/GNSS integration for ADIS16488 IMU
PLOT = 'ON'; % Plot results.
% If a particular parameter is commented above, it is set by default to 'OFF'.
if (~exist('GNSS_DATA','var')), GNSS_DATA = 'OFF'; end
if (~exist('IMU1_DATA','var')), IMU1_DATA = 'OFF'; end
if (~exist('IMU2_DATA','var')), IMU2_DATA = 'OFF'; end
if (~exist('IMU1_INS','var')), IMU1_INS = 'OFF'; end
if (~exist('IMU2_INS','var')), IMU2_INS = 'OFF'; end
if (~exist('PLOT','var')), PLOT = 'OFF'; end
G = 9.80665; % Gravity constant, m/s^2
G2MSS = G; % g to m/s^2
MSS2G = (1/G); % m/s^2 to g
D2R = (pi/180); % degrees to radians
R2D = (180/pi); % radians to degrees
KT2MS = 0.514444; % knot to m/s
MS2KMH = 3.6; % m/s to km/h
fprintf('NaveGo: loading reference dataset from a trajectory generator... \n')
load ref.mat
% ref.mat contains the reference data structure from which inertial
% sensors and GNSS wil be simulated. It must contain the following fields:
% t: Nx1 time vector (seconds).
% lat: Nx1 latitude (radians).
% lon: Nx1 longitude (radians).
% h: Nx1 altitude (m).
% vel: Nx3 NED velocities (m/s).
% roll: Nx1 roll angles (radians).
% pitch: Nx1 pitch angles (radians).
% yaw: Nx1 yaw angle vector (radians).
% DCMnb: Nx9 Direct Cosine Matrix nav-to-body. Each row contains
% the elements of one DCM matrix ordered by columns as
% [a11 a21 a31 a12 a22 a32 a13 a23 a33].
% freq: sampling frequency (Hz).
% IMU data structure:
% t: Ix1 time vector (seconds).
% fb: Ix3 accelerations vector in body frame XYZ (m/s^2).
% wb: Ix3 turn rates vector in body frame XYZ (radians/s).
% arw: 1x3 angle random walks (rad/s/root-Hz).
% arrw: 1x3 angle rate random walks (rad/s^2/root-Hz).
% vrw: 1x3 velocity random walks (m/s^2/root-Hz).
% vrrw: 1x3 velocity rate random walks (m/s^3/root-Hz).
% g_std: 1x3 gyros standard deviations (radians/s).
% a_std: 1x3 accrs standard deviations (m/s^2).
% gb_sta: 1x3 gyros static biases or turn-on biases (radians/s).
% ab_sta: 1x3 accrs static biases or turn-on biases (m/s^2).
% gb_dyn: 1x3 gyros dynamic biases or bias instabilities (radians/s).
% ab_dyn: 1x3 accrs dynamic biases or bias instabilities (m/s^2).
% gb_corr: 1x3 gyros correlation times (seconds).
% ab_corr: 1x3 accrs correlation times (seconds).
% gb_psd: 1x3 gyros dynamic biases PSD (rad/s/root-Hz).
% ab_psd: 1x3 accrs dynamic biases PSD (m/s^2/root-Hz);
% freq: 1x1 sampling frequency (Hz).
% ini_align: 1x3 initial attitude at t(1), [roll pitch yaw] (rad).
% ini_align_err: 1x3 initial attitude errors at t(1), [roll pitch yaw] (rad).
ADIS16405.arw = 2 .* ones(1,3); % Angle random walks [X Y Z] (deg/root-hour)
ADIS16405.arrw = zeros(1,3); % Angle rate random walks [X Y Z] (deg/root-hour/s)
ADIS16405.vrw = 0.2 .* ones(1,3); % Velocity random walks [X Y Z] (m/s/root-hour)
ADIS16405.vrrw = zeros(1,3); % Velocity rate random walks [X Y Z] (deg/root-hour/s)
ADIS16405.gb_sta = 3 .* ones(1,3); % Gyro static biases [X Y Z] (deg/s)
ADIS16405.ab_sta = 50 .* ones(1,3); % Acc static biases [X Y Z] (mg)
ADIS16405.gb_dyn = 0.007 .* ones(1,3); % Gyro dynamic biases [X Y Z] (deg/s)
ADIS16405.ab_dyn = 0.2 .* ones(1,3); % Acc dynamic biases [X Y Z] (mg)
ADIS16405.gb_corr = 100 .* ones(1,3); % Gyro correlation times [X Y Z] (seconds)
ADIS16405.ab_corr = 100 .* ones(1,3); % Acc correlation times [X Y Z] (seconds)
ADIS16405.freq = ref.freq; % IMU operation frequency [X Y Z] (Hz)
% ADIS16405.m_psd = 0.066 .* ones(1,3); % Magnetometer noise density [X Y Z] (mgauss/root-Hz)
% ref dataset will be used to simulate IMU sensors.
ADIS16405.t = ref.t; % IMU time vector
dt = mean(diff(ADIS16405.t)); % IMU sampling interval
imu1 = imu_si_errors(ADIS16405, dt); % Transform IMU manufacturer error units to SI units.
imu1.ini_align_err = [3 3 10] .* D2R; % Initial attitude align errors for matrix P in Kalman filter, [roll pitch yaw] (radians)
imu1.ini_align = [ref.roll(1) ref.pitch(1) ref.yaw(1)]; % Initial attitude align at t(1) (radians).
ADIS16488.arw = 0.3 .* ones(1,3); % Angle random walks [X Y Z] (deg/root-hour)
ADIS16488.arrw = zeros(1,3); % Angle rate random walks [X Y Z] (deg/root-hour/s)
ADIS16488.vrw = 0.029.* ones(1,3); % Velocity random walks [X Y Z] (m/s/root-hour)
ADIS16488.vrrw = zeros(1,3); % Velocity rate random walks [X Y Z] (deg/root-hour/s)
ADIS16488.gb_sta = 0.2 .* ones(1,3); % Gyro static biases [X Y Z] (deg/s)
ADIS16488.ab_sta = 16 .* ones(1,3); % Acc static biases [X Y Z] (mg)
ADIS16488.gb_dyn = 6.5/3600 .* ones(1,3);% Gyro dynamic biases [X Y Z] (deg/s)
ADIS16488.ab_dyn = 0.1 .* ones(1,3); % Acc dynamic biases [X Y Z] (mg)
ADIS16488.gb_corr = 100 .* ones(1,3); % Gyro correlation times [X Y Z] (seconds)
ADIS16488.ab_corr = 100 .* ones(1,3); % Acc correlation times [X Y Z] (seconds)
ADIS16488.freq = ref.freq; % IMU operation frequency [X Y Z] (Hz)
% ADIS16488.m_psd = 0.054 .* ones(1,3); % Magnetometer noise density [X Y Z] (mgauss/root-Hz)
% ref dataset will be used to simulate IMU sensors.
ADIS16488.t = ref.t; % IMU time vector
dt = mean(diff(ADIS16488.t)); % IMU sampling interval
imu2 = imu_si_errors(ADIS16488, dt); % Transform IMU manufacturer error units to SI units.
imu2.ini_align_err = [1 1 5] .* D2R; % Initial attitude align errors for matrix P in Kalman filter, [roll pitch yaw] (radians)
imu2.ini_align = [ref.roll(1) ref.pitch(1) ref.yaw(1)]; % Initial attitude align at t(1) (radians).
% GNSS data structure:
% t: Mx1 time vector (seconds).
% lat: Mx1 latitude (radians).
% lon: Mx1 longitude (radians).
% h: Mx1 altitude (m).
% vel: Mx3 NED velocities (m/s).
% std: 1x3 position standard deviations, [lat lon h] (rad, rad, m).
% stdm: 1x3 position standard deviations, [lat lon h] (m, m, m).
% stdv: 1x3 velocity standard deviations, [Vn Ve Vd] (m/s).
% larm: 3x1 lever arm from IMU to GNSS antenna (x-fwd, y-right, z-down) (m).
% freq: 1x1 sampling frequency (Hz).
% zupt_th: 1x1 ZUPT threshold (m/s).
% zupt_win: 1x1 ZUPT time window (seconds).
% eps: 1x1 time interval to compare IMU time vector to GNSS time vector (seconds).
gnss.stdm = [5 5 10]; % GNSS positions standard deviations [lat lon h] (meters)
gnss.stdv = 0.1 * KT2MS .* ones(1,3); % GNSS velocities standard deviations [Vn Ve Vd] (meters/s)
gnss.larm = zeros(3,1); % GNSS lever arm from IMU to GNSS antenna (x-fwd, y-right, z-down) (m).
gnss.freq = 5; % GNSS operation frequency (Hz)
% Parameters for ZUPT detection algorithm
gnss.zupt_th = 0.5; % ZUPT threshold (m/s).
gnss.zupt_win = 4; % ZUPT time window (seconds).
% Epsilon is a time window within a new GNSS data will be searched at the current INS time.
% It is a very important value. It determines when the Kalman filter will be executed.
gnss.eps = 1E-3;
% The following figure tries to show when the Kalman filter (KF) will be run.
% If a new element from GNSS time vector is available at the current INS time inside the window time
% set by epsilon, the Kalman filter (KF) will be executed.
%
% I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I3
% INS time vector: |---|---|---|---|---|---|---|---|---|---|---|---|
% Epsilon: |-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|
% GNSS time vector: --|------|------|------|------|------|------|
% G1 G2 G4 G5 G6 G7 G8
% KF execution: ^ ^ ^ ^ ^
%
% It can be seen that the KF is not execute at G1 and G6 because of a wrong choice of epsilon.
rng('shuffle') % Reset pseudo-random seed
if strcmp(GNSS_DATA, 'ON') % If simulation of GNSS data is required ...
fprintf('NaveGo: generating GNSS synthetic data... \n')
gnss = gnss_err_profile(ref.lat(1), ref.h(1), gnss); % Transform GNSS manufacturer error units to SI units.
gnss = gnss_gen(ref, gnss); % Generate GNSS dataset from reference dataset.
save gnss.mat gnss
else
fprintf('NaveGo: loading GNSS data... \n')
load gnss.mat
end
rng('shuffle') % Reset pseudo-random seed
if strcmp(IMU1_DATA, 'ON') % If simulation of IMU1 data is required ...
fprintf('NaveGo: generating IMU1 ACCR synthetic data... \n')
fb = acc_gen (ref, imu1); % Generate acc in the body frame
imu1.fb = fb;
fprintf('NaveGo: generating IMU1 GYRO synthetic data... \n')
wb = gyro_gen (ref, imu1); % Generate gyro in the body frame
imu1.wb = wb;
save imu1.mat imu1
clear wb fb;
else
fprintf('NaveGo: loading IMU1 data... \n')
load imu1.mat
end
rng('shuffle') % Reset pseudo-random seed
if strcmp(IMU2_DATA, 'ON') % If simulation of IMU2 data is required ...
fprintf('NaveGo: generating IMU2 ACCR synthetic data... \n')
fb = acc_gen (ref, imu2); % Generate acc in the body frame
imu2.fb = fb;
fprintf('NaveGo: generating IMU2 GYRO synthetic data... \n')
wb = gyro_gen (ref, imu2); % Generate gyro in the body frame
imu2.wb = wb;
save imu2.mat imu2
clear wb fb;
else
fprintf('NaveGo: loading IMU2 data... \n')
load imu2.mat
end
to = (ref.t(end) - ref.t(1));
fprintf('NaveGo: navigation time is %.2f minutes or %.2f seconds. \n', (to/60), to)o)
if strcmp(IMU1_INS, 'ON')
fprintf('NaveGo: INS/GNSS navigation estimates for IMU1... \n')
% Execute INS/GNSS integration
% ---------------------------------------------------------------------
nav1_e = ins_gnss(imu1, gnss, 'dcm');
% ---------------------------------------------------------------------
save nav1_e.mat nav1_e
else
fprintf('NaveGo: loading INS/GNSS integration for IMU1... \n')
load nav1_e.mat
end
if strcmp(IMU2_INS, 'ON')
fprintf('NaveGo: INS/GNSS navigation estimates for IMU2... \n')
% Execute INS/GNSS integration
% ---------------------------------------------------------------------
nav2_e = ins_gnss(imu2, gnss, 'quaternion');
% ---------------------------------------------------------------------
save nav2_e.mat nav2_e
else
fprintf('NaveGo: loading INS/GNSS integration for IMU2... \n')
load nav2_e.mat
end
% INS/GNSS estimates and GNSS data are interpolated according to the
% reference dataset.
[nav1_ref, ref_1] = navego_interpolation (nav1_e, ref);
[nav2_ref, ref_2] = navego_interpolation (nav2_e, ref);
[gnss_ref, ref_g] = navego_interpolation (gnss, ref);
print_rmse (nav1_ref, gnss_ref, ref_1, ref_g, 'INS/GNSS IMU1');
print_rmse (nav2_ref, gnss_ref, ref_2, ref_g, 'INS/GNSS IMU2');
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R. Gonzalez, J.I. Giribet, and H.D. Patiño. NaveGo: a simulation framework for low-cost integrated navigation systems, Journal of Control Engineering and Applied Informatics, vol. 17, issue 2, pp. 110-120, 2015. Download.
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Analog Devices. ADIS16400/ADIS16405 datasheet. High Precision Tri-Axis Gyroscope, Accelerometer, Magnetometer. Rev. B. Download.
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Analog Devices. ADIS16488 datasheet. Tactical Grade Ten Degrees of Freedom Inertial Sensor. Rev. G. Download.
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Garmin International, Inc. GPS 18x TECHNICAL SPECIFICATIONS. Revision D. October 2011. Download.
We would like to thank to many people that have contribute to make NaveGo a better tool:
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Dr. Juan Ignacio Giribet (National University of Buenos Aires, Argentina) for this continuous support on theory aspects of INS/GNSS systems.
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Dr. Charles K. Toth (The Ohio State University, USA), Dr. Allison Kealy, and M.Sc. Azmir Hasnur-Rabiain (both from The University of Melbourne, Australia) for generously sharing IMU and GNSS datasets, and in particular, for Azmir's unselfish support and help.
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Prof. Zhu, Dr. Yang, and Mr. Bo Sun, all from the Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China, for contributing with IMU static measurements to test Allan variance routines.
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Dr. Paolo Dabove and Dr. Marco Piras (both from DIATI, Politecnico di Torino, Italy) for helping to debug NaveGo and suggesting new features.
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