Sensor Fusion Engineer Nanodegree

Professional | Nanodegree | AI & ML

Goal: To Advance Career | Salary: ₹12 lakh | Nanodegree | Proficiency in C++, Linear Algebra, Calculus and Probability| Taught by Udacity in collab. with Mercedes Benz | Duration: 4 Months

This program helps you learn to fuse LiDAR point clouds, radar signatures, and camera images using Kalman Filters to perceive the environment and detect and track vehicles and pedestrians over time.

Please note, you should have good proficiency in C++, Linear Algebra, Calculus and Statistics before you take up this program.

As such, we suggest that you should take up the Intro to Self-Driving Cars Nanodegree program before this program so you learn the fundamentals of driverless car technologies before you take up this specialisation.

What You’ll Learn

  • How to process raw LiDAR data with filtering, segmentation, and clustering to detect other vehicles on the road
  • Fuse camera images together with LiDAR point cloud data. You will extract object features, classify objects, and project the camera image into three dimensions to fuse with LiDAR data
  • Analyse radar signatures to detect and track objects. Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions
  • Fuse data from multiple sources using Kalman filters, and build extended and unscented Kalman filters for tracking nonlinear movement

What You’ll Earn

  • ₹12 lakh per year is the average starting salary for a Sensor Fusion Engineer in India (Source: glassdoor.co.in)
  • $115,000+ per year is the average salary for a Sensor Fusion Engineer in the US (Source: glassdoor.com)

Details

This program will teach you to detect obstacles in LiDAR point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data.

You will combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time.

Here are the skills you will learn:

  • Computer Vision: Point Cloud Data, Non-Linear Motion Tracking, Point Clouds, Object Tracking, Object Clustering, Digital Image Keypoint Descriptors
  • Robotics: Sensor Fusion, Radar Clutter Thresholding, Object Motion Models, Autonomous Vehicle Fluency
  • Data Structures and Algorithms: Kalman Filters, RANSAC Algorithm, Extended Kalman Filters
  • Autonomous Vehicles and Robots: Radar, LiDAR
  • Trigonometry: Basic Trigonometry
  • C++: Point Cloud Library

Applied Learning Project

In this nanodegree, you will work with camera images, radar signatures, and LiDAR point clouds to detect and track vehicles and pedestrians. By the end, you will have created a strong portfolio of projects to demonstrate your skills to employers.

Over the course of the program, you will, among other things:

  • Detect other cars on the road using raw LiDAR data from Udacity’s real self-driving car, Carla! Implement custom Random Sample Consensus (RANSAC) and Euclidean clustering algorithms
  • Detect and track objects from the benchmark KITTI dataset. Classify those objects and project them into three dimensions, and then fuse those projections together with LiDAR data to create 3D objects to track over time
  • Code an Unscented Kalman Filter in C++ to track highly non-linear pedestrian and bicycle motion

Learn More >>

Udacity courses at learn.financedragon.com
Institution: Udacity
Platform: Udacity
Cost: From ~₹80k (₹23k/mth)
Certificate: Nanodegree
Duration: 4 months
Level: Professional
Language: English
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