/scan-matching-localization

Designed two scan matching algorithms, an ICP and NDT, to align point cloud scans from the CARLA simulator and recover the position of a self driving car with LiDAR. My algorithms achieved sufficient accuracy for the entirety of a drive within the simulated environment, updating the vehicle’s location accurately as it moves and obtains new LiDAR data.

Primary LanguageC++

Scan Matching Localization

Designed two scan matching algorithms, an ICP and NDT, to align point cloud scans from the CARLA simulator and recover the position of a self driving car with LiDAR. My algorithms achieved sufficient accuracy for the entirety of a drive within the simulated environment, updating the vehicle’s location appropriately as it moves and obtains new LiDAR data.

Usage

Press the blue button "Desktop". Start one terminal. Run the Carla simulator by using these Unix commands:

su - student # Ignore Permission Denied, if you see student@ you are good
cd /home/workspace/c3-project
./run_carla.sh

Start another terminal. Compile the project by using these Unix commands:

cd /home/workspace/c3-project
cmake .
make

Run the project with the NDT algorithm by using Unix command:

./cloud_loc

Or run the project with the ICP algorithm by using Unix command:

./cloud_loc 2

Once the project is running, click on the map and tap the UP key 3 times, with delays of 1 second between taps. If the green car gets left behind, run the project again and tap the UP key 3 times again. The second run or the third run usually produce better results than the results of the first run. IMPORTANT: Never stop the Carla simulator.

Video Demonstrations

Scan Matching Localization with LiDAR Point Clouds - Algorithm 1: Normal Distributions Transform NDT
https://youtu.be/EOKKcwuBtzo NDT_Passed.png

Scan Matching Localization with LiDAR Point Clouds - Algorithm 2: Iterative Closest Point (ICP)
https://www.youtube.com/watch?v=hZeZAm4jvW4 ICP_Passed.png