/CarND_P6-Extended-Kalman-Filter

utilizing a Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements

Primary LanguageC++

Project 6: Extended Kalman Filter

Udacity - Self-Driving Car NanoDegree

Overview

Utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

The goals / steps of this project are the following:

  • Initialize Kalman filter variables
  • Predict where our object is going to be after a time step Δt
  • Update where our object is based on sensor measurements
  • Make the prediction and update steps repeat themselves in a loop.
  • Calculate root mean squared error comparing the Kalman filter results with the provided ground truth.
  • Achieve RMSE <= [.11, .11, 0.52, 0.52]

Project Deliverables

  • FusionEKF.cpp initializes the filter, calls the predict function, calls the update function
  • kalman_filter.cpp defines the predict function, the update function for lidar, and the update function for radar
  • tools.cpp function to calculate RMSE and the Jacobian matrix

Results

Dataset 1: X: 0.0964 Y: 0.0853 Vx: 0.4154 Vy: 0.4316

Dataset 2: X: 0.0726 Y: 0.0965 Vx: 0.4216 Vy: 0.4932

Dependencies