xfqbuaa / carnd-unscented-kalman-filter-project

Implement an unscented Kalman filter using the CTRV motion model.

Unscented Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

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RMSE results

New data "obj_pose-laser-radar-synthetic-input.txt". RMSE less than requried [.09, .10, .40, .30] and also lower than EKF RMSE.

• 0.0972256
• 0.0853761
• 0.450855
• 0.439588

Python to show NIS and explore data

plotNIS.ipynb

Dependencies

• cmake >= v3.5
• make >= v4.1
• gcc/g++ >= v5.4

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./UnscentedKF path/to/input.txt path/to/output.txt. You can find some sample inputs in 'data/'.
   • eg. ./UnscentedKF ../data/obj_pose-laser-radar-synthetic-input.txt

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

• indent using spaces
• set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please stick to Google's C++ style guide as much as possible.

Generating Additional Data

This is optional!

If you'd like to generate your own radar and lidar data, see the utilities repo for Matlab scripts that can generate additional data.

Project Instructions and Rubric

This information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.