Udacity Self-Driving Car Engineer Nanodegree Program
This project utilizes an Extended Kalman Filter to estimate the state of a moving object with noisy lidar and radar measurements. By passing the radar and ladar readings through the Extended Kalman Filter, an output with a higher degree of certainty and also a lower RMSE value when compared to ground truth values was gotten.
This projects depends on the go num library. To install run:
go get -u gonum.org/v1/gonum/...
The main program can be built and run by doing the following from the project top directory.
go run .\main.go
After passing the sample sensor data through the Extended Kalman Filter, the result can be simulated by running the display.py python script from the same directory, using the command:
python display.py
You will get an output similar to this:
If you'd like to generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.