System: Desktop, Windows 10 Bash on Ubuntu (Linux Subsystem)
The goals / steps of this project are the following:
Utilize a unscented kalman filter to find the state of a moving object according to radar and lidar measurements. Calculate RMSE according to ground-truth data. Project steps:
- Code the Predict, UpdateLidar and UpdateRadar functions to successfully build a Unscented Kalman filter.
- Code the RMSE.
- Initialize the state vector and covariance matrice using the first measurements.
- Predict the object position to current timestep and after recieving measurement update the prediction.
- Call correct measurment function (linear (laser), non-linear (radar)) according to sensor type.
This project involves the Term 2 Simulator which can be downloaded here
Step 1: Install Windows 10 Bash on Ubuntu. Follow the link for a nice guide
Step 2: Install mobaxterm so you can use Sublime (or any other text editor) inside the Bash.
Step 3: Instead of Step 1 and 2 you can follow this thread which also explains how to install Sublime, a nice text editor to be used on Ubuntu.
Started trials with high (for a bycle) acceleration and change of yaw angle std_a: 9 m^2/s^2, std_yawdd: 9 m^2/s^2 and decreased to 3, 1, 0.5 and 0.25. Results were close around 0.5-1 m^2/s^2. Picked 1 m^2/s^2 since marginal benefit drops from this point.
Tried three different situations Lidar Only, Radar Only and Lidar+Radar (Sensor Fusion) to see how the results differ. Lidar gives better result finding the exact location of the bycle while radar is obviously better at determining speed of the bycle. As expected sensor fusion (lidar+radar) gives the best results.
We can see certain amount of decrease in both VX (0.4158 -> 0.3353 and VY (0.4324 -> 0.2254). Though position estimations are close, speed estimations much better with Unscented Kalman Filter.