====== Lab 05 ======

During the lab we will touch on the following topics:

   * Lidar SLAM (Simultaneous Localization and Mapping),
   * ICP (Iterative Closest Point) technique to align point clouds,
   * Absolute Orientation problem as a part of ICP,
   * Point-to-point and point-to-plane metrics,
   * Making ICP robust to outliers.

At the seminar, we will deal with not only simulated examples, but also try to align point cloud scans taken from a real-world environment.


===== Lab task =====

Please, download the lab scripts and data, {{ :courses:aro:tutorials:lab05.zip | lab05.zip}}.

During the lab, we will be using [[https://jupyter.org/install|Jupyter notebook]].
It can be installed by running:
   pip install jupyter

Unzip the prepared files, launch the notebook and follow the instructions in it:
   unzip lab05.zip
   jupyter notebook icp.ipynb


Another option is to run the code using {{ https://colab.research.google.com | Google Colab }}.
We provide a shared notebook: {{ https://colab.research.google.com/drive/1ZQv3O26mx-GT7v354NnLHPZrcYuV061K?usp=sharing | icp.ipynb }}.
You can make a copy of the notebook into your Google Drive in order to be able to edit it and save the changes.

If you are new to Google Colab notebooks, please watch a tutorial to help you to get started, for example:
{{ https://youtu.be/RLYoEyIHL6A | https://youtu.be/RLYoEyIHL6A }}

===== Media =====

Presentation: [[https://docs.google.com/presentation/d/1HScI9C6SeLdO8nne_h2IQDraVZQQOesvgj8AY3otE8A/edit?usp=sharing|slides]].

Point cloud sequences: [[http://ptak.felk.cvut.cz/vras/data/fee_corridor/|data]].

===== Homework 4 assignment =====

Follow the assignment of the homework [[courses:aro:tutorials:homework04|HW4]].