Extended Kalman Filter Project

Self-Driving Car Engineer Nanodegree Program

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Overview

The included source implements the extended Kalman filter in C++. The data folder contains simulated lidar and radar measurements for an object that travels in a figure eight around a sensor. The Kalman filter estimates the object's position and velocity. The metric used to measure the deviation from the ground truth is root mean square error (RMSE).

Compilation

The source can be compiled without error using cmake and make.

The source can be compiled without error using Microsoft Visual Studio.

Results for Included Data Sets

Data set 1 RMSE: 0.0651795 0.0605726 0.544212 0.544226
Data set 2 RMSE: 0.185791 0.190311 0.474852 0.805316

Results Visualization

Data set 1 is visualized using a chart in Microsoft Excel. The green line is the ground truth object location. The orange markers are lidar/radar measurements. The blue line is the EKF estimate of position.

2-D Unity Visualizer

Important: A modified kalman_tracker.py script is included. Usage: python kalman_tracker.py src/ExtendedKF.exe

To optimize the Kalman filter program for real-time use with the Unity Visualizer, the Python script adds a flag to the internal command line (subprocess). This instructs the filter program to utilize only the last 10 items of the input file. The RMSE reports in the Visualizer therefore correspond to the 10 most recent measurements.

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1 of 3: Below is the fused result of a run with Lidar and Radar enabled. The green markers indicate the EKF estimate of position:

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2 of 3: Below is the result with only Radar enabled:

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3 of 3: Below is the result with only Lidar enabled:

Dependencies

• cmake >= 3.5
• All OSes: click here for installation instructions
• make >= 4.1
  • 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]((https://developer.apple.com/xcode/features/)
  • Windows: recommend using MinGW

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: ./ExtendedKF path/to/input.txt path/to/output.txt. Sample inputs can be found in 'data/'.
   • eg. ./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt output.txt

A debug version can be made using: cmake -DCMAKE_BUILD_TYPE=Debug .. && make

Code Style

Conforms to Google's C++ style guide.