Extended Kalman Filter Project

Self-Driving Car Engineer Nanodegree Program

Overview

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This project utilizes a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements. To acheive this I used a Kalman filter, lidar measurements and radar measurements to track the bicycle's position and velocity around a vehicle. Goal of the project is to obtain RMSE values that are lower than the desired tolerance.

Project Goals

The goals / steps of this project are the following:

• Build a Kalman Filter (KF) for tracking using Lidar measurments
• Build an Extended Kalman Filter(EKF) for tracking using Radar measurments
• Test algorithm against Dataset-1 in the simulator
• Output coordinates px, py, vx, and vy RMSE should be less than or equal to the values [.11, .11, 0.52, 0.52]

Project Files

The repository consists of the following files:

• ./Docs/ - Supporting documentation
• ./data/obj_pose-laser-radar-synthetic-input.txt - Text file contating measurements from the two sensors
• ./ide_profiles - Editor profiles
• ./src/Eigen - Eigen Library
• ./src/CMakeLists.txt - Code compilation
• ./src/main.cpp - communicates with the Term 2 Simulator receiving data measurements, calls a function to run the Kalman filter, calls a function to calculate RMSE
• ./src/FusionEKF.cpp - Initializes the filter, calls the predict function, calls the update function
• ./src/tools.cpp - function to calculate RMSE and the Jacobian matrix
• ./src/json.cpp - Structuring to communicate with simulator
• ./src/kalman_filter.cpp - Defines the predict function, the update function for lidar, and the update function for radar
• ./src/FusionEKF.h - Header file for FusionEKF.cpp
• ./src/kalman_filter.h - Header file for kalman_filter.cpp
• ./src/measurement_package.h - Header file for measurement_package.cpp
• ./src/tools.h - Header file for tolls.cpp

Project Dependencies

• cmake: 3.5
• make: 4.1 (Linux and Mac), 3.81 (Windows)
• gcc/g++: 5.4
• uWebSocketIO: Use install-ubuntu.sh

Project Simulator

This project involves Tracking Simulator which can be downloaded here

Lidar measurements are red circles, radar measurements are blue circles with an arrow pointing in the direction of the observed angle, and estimation markers are green triangles.

Project Build Instructions

1. mkdir build
2. cd build
3. cmake ..
4. make
5. ./ExtendedKF

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Dataset

The dataset consists of simulated lidar and radar measurements detecting a bicycle that travels around the vehicle.

Data file:

• obj_pose-laser-radar-synthetic-input.txt

Data Flow:

1. The measuremennt processor/matlab simulator is generating the FUSION .txt file: "data/obj_pose-laser-radar-synthetic-ukf-input.txt"; OR "../matlab_examples/obj_pose-laser-radar-synthetic-ukf-input.txt";

The Input file format is: #L(for laser) meas_px meas_py timestamp gt_px gt_py gt_vx gt_vy #R(for radar) meas_rho meas_phi meas_rho_dot timestamp gt_px gt_py gt_vx gt_vy

Example: R 8.60363 0.0290616 -2.99903 1477010443399637 8.6 0.25 -3.00029 0 L 8.45 0.25 1477010443349642 8.45 0.25 -3.00027 0

2. The EKF Algorithm reads form file reads all the lines and generates measurement structures
3. The MeasurementProcessor() is called with individual measurements (one by one). The results are saved (Attention: no file processing routines are used inside MeasurementProcessor() all the file processing routines are in the main function So the data read/write is decoupled from the algorithm
4. The results are saved in an output file: "data/obj_pose-laser-radar-ekf-output.txt"

Output file format: est_px est_py est_vx est_vy meas_px meas_py gt_px gt_py gt_vx gt_vy

Example: 4.53271 0.279 -0.842172 53.1339 4.29136 0.215312 2.28434 0.226323 43.2222 2.65959 0.931181 23.2469 4.29136 0.215312 2.28434 0.226323

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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Results

The project goal of keeping RMSE value less than or equal to the values [.11, .11, 0.52, 0.52] was succesfully acheived.