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Navigation Toolbox User’s Guide.pdf
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Set the sampling rates. In a typical system, the accelerometer and gyroscope in the IMU run at relatively high sample rates. The complexity of processing data from those sensors in the fusion algorithm is relatively low. Conversely, the GPS runs at a relatively low sample rate and the complexity associated with processing it is high. In this fusion algorithm the GPS samples are processed at a low rate, and the accelerometer and gyroscope samples are processed together at the same high rate.
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Navigation Toolbox™
User's Guide
R2020a

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Navigation Toolbox™ User's Guide
© COPYRIGHT 2019–2020 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used or copied
only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form
without prior written consent from The MathWorks, Inc.
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the federal government of the United States. By accepting delivery of the Program or Documentation, the government
hereby agrees that this software or documentation qualies as commercial computer software or commercial computer
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more information.
Revision History
September 2019 Online only New for Version 1.0 (R2019b)
March 2020 Online only Rereleased for Version 1.1 (R2020a)

Navigation Featured Examples
1
Estimate Position and Orientation of a Ground Vehicle ............... 1-3
Pose Estimation From Asynchronous Sensors ..................... 1-11
Inertial Sensor Noise Analysis Using Allan Variance ................ 1-16
Estimate Orientation Through Inertial Sensor Fusion ............... 1-27
IMU and GPS Fusion for Inertial Navigation ...................... 1-36
IMU Sensor Fusion with Simulink ............................... 1-44
Magnetometer Calibration ..................................... 1-46
Remove Bias from Angular Velocity Measurement .................. 1-55
Detect Noise in Sensor Readings with Residual Filtering ............ 1-59
Estimate Orientation and Height Using IMU, Magnetometer, and
Altimeter ................................................. 1-65
Estimate Orientation with a Complementary Filter and IMU Data ..... 1-69
Estimating Orientation Using Inertial Sensor Fusion and MPU-9250 .. 1-77
Rotations, Orientation, and Quaternions ......................... 1-87
Lowpass Filter Orientation Using Quaternion SLERP .............. 1-102
Introduction to Simulating IMU Measurements ................... 1-106
Logged Sensor Data Alignment for Orientation Estimation ......... 1-118
Generate O-centered IMU Readings ........................... 1-125
Estimate Robot Pose with Scan Matching ........................ 1-130
Localize TurtleBot Using Monte Carlo Localization ................ 1-136
Compose a Series of Laser Scans with Pose Changes .............. 1-149
Minimize Search Range in Grid-based Lidar Scan Matching Using IMU
........................................................ 1-153
iii
Contents

Visual-Inertial Odometry Using Synthetic Data ................... 1-158
Reduce Drift in 3-D Visual Odometry Trajectory Using Pose Graphs .. 1-167
Create Egocentric Occupancy Maps Using Range Sensors .......... 1-170
Build Occupancy Map from Lidar Scans and Poses ................ 1-175
Create Egocentric Occupancy Map from Driving Scenario Designer .. 1-176
Build Occupancy Map from Depth Images Using Visual Odometry and
Optimized Pose Graph ...................................... 1-180
Implement Simultaneous Localization And Mapping (SLAM) with Lidar
Scans ................................................... 1-183
Implement Online Simultaneous Localization And Mapping (SLAM) with
Lidar Scans .............................................. 1-190
Perform SLAM Using 3-D Lidar Point Clouds ..................... 1-197
Plan Mobile-Robot Paths using RRT ............................ 1-205
Moving Furniture in a Cluttered Room with RRT .................. 1-211
Motion Planning with RRT for a Robot Manipulator ............... 1-216
Dynamic Replanning on an Indoor Map ......................... 1-223
Highway Lane Change ........................................ 1-229
Path Following with Obstacle Avoidance in Simulink® ............. 1-248
Obstacle Avoidance with TurtleBot and VFH ..................... 1-252
Optimal Trajectory Generation for Urban Driving ................. 1-254
Navigation Topics
2
Model IMU, GPS, and INS/GPS ................................... 2-2
Inertial Measurement Unit .................................... 2-2
Global Positioning System ..................................... 2-4
Inertial Navigation System and Global Positioning System ............ 2-6
Congure Time Scope MATLAB Object ............................ 2-8
Signal Display .............................................. 2-8
Multiple Signal Names and Colors .............................. 2-9
Congure Scope Settings ..................................... 2-9
Use timescope Measurements ................................ 2-10
Share or Save the Time Scope ................................ 2-13
iv Contents

Scale Axes ............................................... 2-13
Occupancy Grids ............................................. 2-14
Overview ................................................ 2-14
World, Grid, and Local Coordinates ............................. 2-14
Ination of Coordinates ..................................... 2-15
Log-Odds Representation of Probability Values .................... 2-19
Execute Code at a Fixed-Rate ................................... 2-22
Introduction .............................................. 2-22
Run Loop at Fixed Rate ..................................... 2-22
Overrun Actions for Fixed Rate Execution ........................ 2-22
Particle Filter Workow ....................................... 2-25
Estimation Workow ........................................ 2-25
Particle Filter Parameters ...................................... 2-29
Number of Particles ........................................ 2-29
Initial Particle Location ...................................... 2-30
State Transition Function .................................... 2-31
Measurement Likelihood Function ............................. 2-32
Resampling Policy ......................................... 2-32
State Estimation Method .................................... 2-33
Pure Pursuit Controller ........................................ 2-34
Reference Coordinate System ................................. 2-34
Look Ahead Distance ....................................... 2-34
Limitations ............................................... 2-35
Monte Carlo Localization Algorithm ............................. 2-36
Overview ................................................ 2-36
State Representation ....................................... 2-36
Initialization of Particles ..................................... 2-38
Resampling Particles and Updating Pose ......................... 2-40
Motion and Sensor Model .................................... 2-41
Vector Field Histogram ........................................ 2-45
Robot Dimensions .......................................... 2-45
Cost Function Weights ...................................... 2-47
Histogram Properties ....................................... 2-47
Tune Parameters Using show ................................. 2-49
Navigation Block Examples
3
Convert Coordinate System Transformations ....................... 3-2
v
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