Team Timelaps: Udacity Challenge 2017

Results

This code base achieved 14th out of 2000 teams (top 1%) !!

Dependencies

This code is designed to work with the data provided by Udacity for the 2017 Self Driving Car Challenge.

This code has been developed on a mix of OS/X Sierra and Ubuntu 16.04 LTS hosted on a MacBook Pro, a VirtualBox VM and run on Google Cloud Platform.

The following packages are dependencies:

• Ubuntu 16.04
• ROS Kinetic
• Python 2.7
• OpenCV (ROS Provided)
• Numpy (Latest)
• Pandas (Latest)
• MayAvi (Provided in Ubuntu 16.04)
• matplotlib
• Catkin (Provided by ROS Kinetic)
• Velodyne drivers (see here for installation)

Training

To train the model, perform the following steps:

Prepare the data

For each training bag, follow the following steps:

1. Run the bag-to-kitti.sh script from the Udacity provided code
2. Follow the ROS instructions here to create a bag containing Velodyne points
3. Run the script extract_pointclouds.py with the pointcloud bag generated above and the same output directory used for bag_to_kitti.sh previously. Note this will also extract Radar points if they are included in the bag.

Training the model

To train the model, run the following commands:

TODO!!!

Predicting

The following steps will set up the ROS environment to perform predictions on incoming sensor messages:

1. Run roscore
2. In a seperate terminal, run rosrun nodelet nodelet standalone velodyne_pointcloud/CloudNodelet to convert Velodyne scans messages into PointCloud2 messages
3. In a seperate window, run the tracklet generator todo...
4. In a seperate terminal, run predictortodo...
5. Play the test bag, or data from your car's sensors if you have one!!

Improvements / Next steps

1. Rewrite the code in C
2. Write up a report on my findings