Map Fusion for Collaborative UAV SLAM
This project was a semester project I did at the Vision for Robotics Lab (www.v4rl.ethz.ch) at the Swiss Federal Institute of Technology (ETH) durgin my master studies in Information Technology and Electrical Engineering.
The report that I had to write for this semester project is written in LaTeX and located in the folder report.
The presentation I gave is located in the folder presentation. There is also an extended version, which does describe certain things in more detail, e.g. the different optimization algorithms.
Python script to calculate the root mean squared error (RMSE) from a recorded experiment.
Python script to calculate the root mean squared error (RMSE) from a recorded experiment with an estimated time offset.
Receives ground truth positions and writes it into a text file.
Republishes topics with retimed timestamps.
Modifid multi client SLAM system, in which the proposed approaches of this semester project are implemented.
The text files with the recorded timestamps and coordinates of each experiment/dataset are in the folder evaluation.
The data sets are "close", "far", "frontal", and "uav". "vicon" represents the ground truth. mX_skY describes how many KeyFrameMatches were required to fuse the maps (X) and how many KeyFrames were skipped (Y) after a KeyFrameMatch was detected.
The relevant research papers and some slides are in the folder papers.