sketchc89 / unscentedkalmanfilter-p7

Unscented Kalman Filter for Udacity self-driving car nanodegree

Unscented Kalman Filter Project Starter Code

Self-Driving Car Engineer Nanodegree Program

This project utilizes an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

Results

The UnscentedKalmanFilter program was run on both datasets. The results are below.

Acceleration and yaw rate process noise were tuned manually. Results are recorded in this csv which can be opened in excel/calc.

Areas for improvement

• Testing

Protocol

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program

• ["sensor_measurement"] => the measurement that the simulator observed (either lidar or radar)

OUTPUT: values provided by the c++ program to the simulator

• ["estimate_x"] <= kalman filter estimated position x
• ["estimate_y"] <= kalman filter estimated position y
• ["rmse_x"]
• ["rmse_y"]
• ["rmse_vx"]
• ["rmse_vy"]

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Other Important Dependencies

• cmake >= 3.5
  • All OSes: click here for installation instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
  • Mac: install Xcode command line tools to get make
  • Windows: Click here for installation instructions
• gcc/g++ >= 5.4
  • Linux: gcc / g++ is installed by default on most Linux distros
  • Mac: same deal as make - install Xcode command line tools
  • Windows: recommend using MinGW
• pthread library for spdlog

Basic Build Instructions

1. Clone this repo.
2. Make a build directory: mkdir build && cd build
3. Compile: cmake .. && make
   • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
4. Run it: ./UnscentedKF

One change was made to CMakeLists.txt to support spdlog, a C++ logging library that eliminated the need for print statement debugging.