sgalkin / CarND-T2P2

Udacity CarND Unscented Kalman Filter Project

Unscented Kalman Filter Project

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

Implementation requires obtaining RMSE values that are lower that the tolerance outlined in the specification.

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Usage

./UnscentedKF [-R] [-L]

• -R - use only radar measurements
• -L - use only laser measurements
• default - use both types of measurement

Dependencies

Runtime

• Term 2 Simulator

Tools

• 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, clang
  • 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

Libraries not included into the project

• uWebSocketIO == v0.13.0
  • Ubuntu/Debian: the repository includes install-ubuntu.sh that can be used to set up and install uWebSocketIO
  • Mac: the repository includes install-mac.sh that can be used to set up and install uWebSocketIO
  • Windows: use either Docker, VMware, or even Windows 10 Bash on Ubuntu

Libraries included into the project

• JSON for Modern C++ - JSON parser
• Catch2 - Unit-testing framework
• ProgramOptions.hxx - Single-header program options parsing library for C++11
• Eigen - C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms

Build

0. Clone this repo.
1. mkdir build
2. cd build
3. cmake .. -G "Unix Makefiles"
4. make
5. make test - optional

Protocol

The project uses uWebSocketIO request-response protocol to communicate 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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TODOs

• Experiment more with Kalman filter state initialization. Eg. use truncated normal distribution with 0 mean to speed, turn rate, and acceleration
• Add more unit/functional tests