Autonomously Following Forest Paths with a Mobile Robot using Semantic Segmentation#


This project is Pieter Fiers and Simon Germeau’s professional bachelor’s thesis for UCLL’s Aplied Informatics degree.

We created a rover that is capable of autonomously following forest paths.

We used a Semantic Segmentation CNN, as opposed to the classification CNN used by Giusti et al.1 and Smolyanskiy et al.2. We believe this enables interesting future expansions, like higher-level decision making about path intersections, and the mapping of road geometries.

Repository structure#

The repository is structured according to the four main stages of our project:

Dataprep - Documentation about, and the scripts we used for, the processing and labelling of data used for training.

Model - This folder contains the machine learning process we used to train our CNN.

Rover - Documentation regarding the hardware aspect of our rover.

ROS - All documentation, ROS (Robot Operating System) nodes, and extra files needed to make the rover drive itself.


As pdf or as pptx   source

Demo videos#

All demo videos combined (see YouTube description for timestamps):

Collage of the pathy rover and prediction for a forest path


Smolyanskiy et al., “Toward Low-Flying Autonomous MAV Trail Navigation using DeepNeural Networks for Environmental Awareness” May 2017.


Giusti et al., “A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots” Dec 2015.