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SLAM Integrated Mobile Platform

University project to integrate a laserscanner, GNSS antenna and remote controled vehicle in order to perform SLAM and georeference the results.

GitCover

Demo Website: http://ifpwww.ifp.uni-stuttgart.de/philipp/Simp-Viewer/SimpViewer.html

image

Demo Video: http://youtube.com/watch?v=GBuS46cAL-4

Overview

This repository includes the processing code for data collected with the SIMP vehicle as well as some example data to run the program. Cloning the master branch will give you the following structure:

 your_project_folder/
	│
	├── Code/  
        │   ├── functions/  
	│   ├── Georeferencing.m 
	└── Data/  
	    ├── GNSS Trajectories/  
	    ├── GoPro Images/  
            │   └── GoProSchloss/  
 	    ├── Scan Trajectories/  
	    ├── Scans/  
	    └── output/

How to use

The Georeferencing function includes the main code that will be used to call all functions related with this project. To process the example data simply run Georeferencing.m and select the corresponding data when prompted.

The data can be downloaded from Google-Drive via this link (~ 2GB): https://drive.google.com/drive/folders/1lvRP46lA1Gb7gnMfUR8oFm5_vJhGkE-_?usp=sharing

Simply download the SIMP Data folder and integrate the structure into your_project_folder/Data/

The following MATLAB Add-Ons need to be installed:

  • Aerospace Toolbox (by MathWorks)
  • Mapping Toolbox (by MathWorks)
  • Signal Processing Toolbox (by MathWorks)
  • Symbolic Math Toolbox (by MathWorks)
  • Lidar Toolbox (by MathWorks)
  • Image Processing Toolbox (by MathWorks)
  • lasdata (by Teemu Kumpumäki)

After successfully running the code, the output pointcloud and trajectory can be found in your_project_folder/Data/output/

Processing code summary

The main steps of the program are:

  • Loading all necessary data (see Data folder)
  • Calculating Time Offset (to connect Scan and GNSS data)
  • Coarse trajectory match (as preparation for the ICP-based accurate matching)
  • Accurate trajectory match (using temporally closest points of both trajectories)
  • Estimate point matching accuracy (statistical values for relative accuracy of matching algorithm)
  • Transform point cloud (apply combined transformation)
  • Colorize point cloud (uses GoPro Data to determine colors)
  • Remove moving objects (to clean up the point cloud)
  • Ground classification (to seperate the points by ground and get some semantic information)
  • Save final cloud (in Earth-Centered-Earth-Fixed (ECEF) coordinates)