Sensor Fusion Engineer by Udacity: Fee, Duration, How to Apply

Quick Facts

particular	details
Collaborators Mercedes Benz	Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based

Course overview

The supply of professionals with advanced sensor fusion skills has remained behind the demand expressed by the industry. Hence, an investment in this Nanodegree program will set you up in the league of candidates that are in short supply, and therefore very likely to find economically and professionally rewarding packages in the global value chain.

This program revolves around 4 uniquely curated, industrial robotics projects that test the most relevant problem statements of this industry. Thus, once you brainstorm through these wholesome, exciting, and complex scenarios, you would come out on the other side having answered a lot of the questions prospective recruiting companies look for, in their candidates.

Typically, studying for 10 hours a week, a student is able to graduate from this program in 4 months. For those who have limited background in one of the prerequisites, candidates should consider the Intro to Self-driving Cars Nanodegree program. This will help them in laying foundation so that you can enroll for this program thereafter.

Course and certificate fees

The fees for the course Sensor Fusion Engineer is -

Head	Amount in INR
All Access monthly	Rs. 9,225 /month

certificate availability

Yes

certificate providing authority

Udacity

Who it is for

Python programmer

The Sensor Fusion Engineer Nanodegree program is apt for professionals aspiring to learn about radar, lidar, and camera data, to subsequently fuse it together for real-world application. Professionals with the following capabilities are likely to gain the most from this course:

Practical coding experience of an Object Oriented Programming language such as Python
High-school probability applications
Linux command lines
Expertise in Linear Algebra
Comfortable with Calculus

Eligibility criteria

Work experience

The program needs no work experience, and is thus open for all.

Education

For the best learning experience, it is advisable to have working knowledge of C++ or an equivalent programming language based on the principles of an Object Oriented setup. Additionally, it would be very desirable to have the basics of probabilities, calculus, and linear algebra to be in place.

Certification qualifying details

The program requires the students to clear the projects associated with all the 4 modules to be duly cleared in order to graduate. Whenever a submission is made, it will be evaluated by the designated faculty and relevant feedback be provided. The submissions not clearing in the first attempt will need to be re-attempted till they match the minimum standards.

What you will learn

Knowledge of engineering

Upon graduating from the Sensor Fusion Engineer Nanodegree program, the participants will learn these concepts:

Design intelligent systems to detect vehicles on the road with the use of segmentation, filtering, and clustering of raw lidar data.
Application of Euclidean clustering to differentiate between vehicles and obstacles.
Get equipped with radar signatures which will help in calculating velocity and orientation by taking into account radial velocity distortions.
Become proficient in the in the nuances associated with thresholds to ascertain and subsequently eliminate false positive.
Recognize lidar point cloud data to extract information from camera images to accurately arrive at the object’s motion and direction of travel.
Utilize 3-D image projection techniques in collaboration with lidar data to setup a motion model.
Gain a deep understanding of data fusion from multiple sources with the assistance of Kalman filters.
Expedite the implementation of the feedback loop which will enable a dynamically updated version of Kalman filters. Moreover, unscented Kalman filters can then be built.

The syllabus

Lidar

Introduction of Lidar & Point Clouds

Lidar data representation
Visualize Lidar data
Work with a simulator to create PCD

Point Cloud Segmentation

Use the RANSAC algorithm for planar model fitting
Use PCL to segment point clouds

Clustering Obstacles

Use a KD-tree to store point cloud data
Use PCL to cluster obstacles
Apply building boxes around clusters
Implement Euclidean Clustering to find clusters

Working with Real Point Cloud Data

Work with real self-driving car PCD
Play back multiple PCD files
Filter PCD
Apply Point cloud processing to detect obstacles

Course Project – Lidar Obstacle Detection

Filter, segment, and cluster real point cloud data to detect obstacles in a driving environment

Radar

Introduction to Radar

Handle real radar data
Determine the appropriate sensor specifications for a task
Calculate object headings and velocities

Radar Calibration

Filter noise from real radar sensors
Correct radar data to account for radial velocity

Radar Detection

Predict the location of occluded objects
Threshold radar signatures to eliminate false positives

Course Project – Radar Obstacle Detection

Calibrate, threshold, and filter radar data to detect obstacles in real radar data

Camera

Sensor Fusion and Autonomous Driving

Understand the SAE levels of autonomy
Compare typical autonomous vehicle sensor sets including Tesla and Mercedes
Compare camera, lidar and radar using a set of industry-grade performance criteria

Camera Technology and Collision Detection

Understand how light forms digital images and which properties of the camera (e.g. aperture, focal length) affect this formation
Manipulate images using the OpenCV computer vision library
Design a collision detection system based on motion models, lidar and camera measurements

Feature Tracking

Match features between images to track objects over time using state-of-the-art binary descriptors
Detect features from objects in a camera image using state-of-the-art detectors and standard methods

Camera and Lidar Fusion

Project 3D lidar points into a camera sensor
Use deep-learning to detect vehicles (and other objects) in camera images
Create a three-dimensional object from lidar and camera data

Course Project – Camera and Lidar Fusion

Detect and track objects in 3D space from the benchmark KITTI dataset based on camera and lidar measurements. Compute time-to-collision based on both sensors and compare the results. Identify the best combination of key point detectors and descriptors for object tracking

Kalman Filters

Merge data from multiple sources
Construct Kalman filters
Improve tracking accuracy
Reduce sensor noise

Lidar and Radar fusion with Kalman filters

Handle both radar and lidar data
Build a Kalman Filter in C++

Extended Kalman Filters

Predict when non-linear motion will cause errors in a Kalman filter
Construct Jacobian matrices to support EKFs
Program an extended Kalman filter to cope with non-linear motion

Unscented Kalman Filters

Estimate when highly nonlinear motion might break even an extended Kalman Filter
Create an unscented Kalman Filter to accurately track non-linear motion

Course Project – Unscented Kalman Filters Project

Put your skills to the test! Code an Unscented Kalman Filter in C++ in order to track highly non-linear pedestrian and bicycle motion

Admission details

The enrolment steps for the Sensor Fusion Engineer Nanodegree program are:

Enrolment

This Nanodegree program comes with a free trial that can be availed by all users. You can access this from the course page by signing in with your Google or Facebook account. For others, please proceed with signing up with a new account.

Payment

The standard accessibility of 4 months comes in-built with the tuition fee of $1356 USD. Moreover, it will be charged to your preferred banking route only after the first 7 days of the free trial. Moving on, if the candidate hasn’t cleared all the requisite papers within the 4-month period, the cost for each subsequent is $399 USD. This will be applicable till the student clears all the projects to the standards deemed fit.

LMS

The LMS access is the last step in the chain of events. This is made available within a few minutes of the receipt of payment by us.

Scholarship Details

As of now, there is no provision for a scholarship. However, depending on if you pay upfront or as you go, the total price will incur a discount of 15%.

Evaluation process

The Nanodegree is offered when all assignments at the end of each module are cleared by the students. This leads to the access to the next module.

How it helps

The founding stones of the program are designed based on thorough primary market research. The elements being offered and their depth has been taken from the most employable skills that have demand in today’s job market.

Real world projects are ingrained very well into the Robotics Software Engineer Nanodegree Program. All the 4 modules have been curated by industry experts to give participants confidence for having immersed in it.

Instructors

Mr David Silver
Lead
Udacity

Other Bachelors, MBA

Mr Stephen Welch
Instructor
Udacity

Ph.D

Mr Andreas Haja
Professor
Freelancer

Ph.D

Mr Abdullah Zaidi
Instructor
UMD

M.S

Mr Aaron Brown
Instructor
Freelancer

FAQs

Who are the course faculty?

Following is the course faculty:

Dr. Stephan Welch (Instructor, PhD in Mathematics)
Dr. Andreas Haja (Instructor, ex Volkswagen and Bosch)
Mr. Abdullah Zaidi (Instructor, M.S. – University of Maryland)
Mr. David Silver (Curriculum Lead, ex Ford, MBA Stanford)

Is there an acceptance criteria for this program?

No, the objectives of the program have been laid out. Please evaluate if those match with your professional goals and proceed to enroll.

When does the course commence?

The approach to the Learning Management System is made available on receipt of the payment towards the course fee. Thus, the course commencement depends on the candidate.

How long has this course been designed to run?

Over the 4-month access, it is envisaged that the students should be able to graduate from this Nanodegree program. Should there be a need to extend, the same can be done by paying a monthly fee.

Are there any support functions provided for the Robotics Software Engineer program?

Absolutely, a department of technical experts will be assigned to your batch for any and all product support that may be sought from you. Their contact mechanism will be explained after on-boarding is completed.

Is there placement assistance provided as part of this program?

Measures are in place to widen the professional network for our students through strategic networking events from global leaders. Moreover, in-depth interview preparation in-person as well telephonically will be made available.

What kinds of jobs are seen after the completion of this program?

Following are some common job profiles: Sensor Fusion Engineer, Automated Vehicle Engineer, Self-driving car engineer, System integration engineer, Perception engineer, Imaging engineer, and Object tracking engineer.

Is there a provision for a demo/preview session for this course?

Yes, the course preview can be accessed from the homepage of the Nanodegree program.

What software do I need?

As the coding will be done on our virtual machines through the browsers, you wouldn’t need to install anything on your own computer. However, if you would like to complete projects on your machine, please install C++ version 11 and Point Cloud Library 1.7. Guidance will be given on how and where to get these from.