Repository used to condense all code related to Tupã's autonomous vehicle adventure.
This repository is divided into four main modules: Image Processing, SLAM, ROS and Telemetry-Api.
Specific information about how to execute each module is specified in each module's README.
The improc module contains a Python algorithm for cone identification. It's built using
the library OpenCV.
- Camera calibration
- Color picking in HSV spectrum based on sampled images or input video
- Cone detection algorithm
- Using input images
- Using live video
- ZMQ connection with SLAM
The slam module contains a Python and Numpy implementation of FastSLAM 2.0.
It's able to simultaneously locate and map the robot.
- FastSLAM 2.0 implementation
- ZMQ connection to fetch input data
- API handler to pass information to our telemetry-api
The ros module contains a simulation environment built using ROS Noetic. It contains
a differential robot implementation and allows testing of the previously mentioned systems
It's able to simultaneously locate and map the robot.
The telemetry-api contains a simple REST API built using Flask to receive data
from our vehicle. It's used for real-time data visualization.