[RADAR + LIDAR Sensor Fusion Processing Pipeline]

NANODEGREE COURSE (Self-Driving Car Engineer - Sensor Fusion)

Stater Code

LESSON 1 : Introduction and Sensors

Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.

• Intuition of Kalman Filter for Self-Driving-Car Applications

LESSON 2 : Kalman Filters

Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.

• Sensor Fusion and Object Tracking using an Extended Kalman Filter Algorithm — Part 1 : mithi

• Sensor Fusion and Object Tracking using an Extended Kalman Filter Algorithm — Part 2 : mithi

• Sensor Fusion Algorithms For Autonomous Driving: Part 1 — The Kalman filter and Extended Kalman Filter

• UDACITY SDCE Nanodegree Term 2: Kalman Filters for Sensor Fusion

• Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter

• Kalman Filter: Predict, Measure, Update, Repeat.
• Make sense of Kalman Filter
• What is a Kalman filter and why is there an unscented version?
• How a Kalman filter works, in pictures
• Udacity Self-Driving Cars: Extended Kalman Filters — my bits
• Extended Kalman Filters for Dummies

LESSON 3 : C++ Checkpoint

• Are you ready to build Kalman Filters with C++? Take these quizzes to find out.

LESSON 4 : Lidar and Radar Fusion with Kalman Filters in C++

• In this lesson, you'll build a Kalman Filter in C++ that's capable of handling data from multiple sources. Why C++? Its performance enables the application of object tracking with a Kalman Filter in real-time.

LESSON 5(Project) : Extended Kalman Filter Project

In this project, you'll apply everything you've learned so far about Sensor Fusion by implementing an Extended Kalman Filter in C++!

• Udacity Self-Driving Car Nanodegree Project 6 — Extended Kalman Filter

• SDC ND Project 1 — Finding Lane Lines With Kalman Filter

LESSON 6 : Unscented Kalman Filters

While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!

• How the Unscented Kalman Filter Got Its Name

LESSON 7(Project) : Unscented Kalman Filter Project

Put your skills to the test! Use C++ to code an Unscented Kalman Filter capable of tracking non-linear motion.

• Udacity Self-Driving Car Nanodegree Project 7 — Unscented Kalman Filter

CarND-Mercedes-SF-Utilities

• Tools for Sensor Fusion processing.
• visualizing and analyzing your data

파일 내용

[SENSOR ID] [SENSOR RAW VALUES] [TIMESTAMP] [GROUND TRUTH VALUES]

Example 1: line with three measurements from a radar sensor in polar coordinate followed by a timestamp in unix time followed by the the "ground truth" which is actual real position and velocity in cartesian coordinates (four state variables)

R 8.46642 0.0287602 -3.04035 1477010443399637 8.6 0.25 -3.00029 0 (R) (rho) (phi) (drho) (timestamp) (real x) (real y) (real vx) (real vy)

Example 2: line with two measurements from a lidar sensor in cartesian coordinates followed by a timestamp in unix time followed by the the "ground truth" which is the actual real position and velocity in cartesian coordinates (four state variables)

L 8.44818 0.251553 1477010443449633 8.45 0.25 -3.00027 0