For a full project overview, see the wiki!
A particle filter implementing carpet-based localisation.
Inputs to particle filter are:
- odometry: pose delta since previous update
- color: current detected carpet color under robot
- map: grid map of carpet colors
Output of particle filter is the current pose (2D) of the robot
Figure: playback of particle filter using simulated input data (see simulator.py), driving in a loop on a small map. Legend:
- green arrow: ground truth pose
- large red arrow: estimated pose from particle filter
- small red arrows: particle filter particles
- blue/grey/beige background: carpet color pattern map
Figure: localisation test in target environment (carpeted office).
For ROS wrapper around this package, see carpet_localisation_ros.
For classifier trained to distinguish the four colors of carpet in the target environment, see carpet_color_classification
Note: for an overview of the broader project, see the wiki.
git clone [email protected]:tim-fan/carpet_localisation.git
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt
pip install -e .
pytest .Or to view output of plotting tests:
pytest . --show_plot True