Course Summary
This course will teach you how to combine data from multiple sensors to perceive the environment, detect obstacles, localize your vehicle, and navigate complex scenarios. You'll learn how to use Kalman filters, lidar, radar, and computer vision techniques to fuse sensor data and detect objects in real-time.Key Learning Points
- Learn how to combine data from multiple sensors to perceive the environment
- Detect obstacles, localize your vehicle, and navigate complex scenarios
- Use Kalman filters, lidar, radar, and computer vision techniques to fuse sensor data and detect objects in real-time
Job Positions & Salaries of people who have taken this course might have
- Sensor Fusion Engineer
- USA: $110,000
- India: ₹1,000,000
- Spain: €50,000
- Autonomous Vehicle Engineer
- USA: $120,000
- India: ₹1,200,000
- Spain: €60,000
- Robotics Engineer
- USA: $100,000
- India: ₹800,000
- Spain: €40,000
Related Topics for further study
Learning Outcomes
- Understand how to use sensor fusion to perceive the environment
- Learn how to detect obstacles and localize your vehicle in real-time
- Be able to navigate complex scenarios using sensor data
Prerequisites or good to have knowledge before taking this course
- Experience with programming in C++ and Python
- Knowledge of linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Project-based
Similar Courses
- Self-Driving Car Engineer Nanodegree
- Robotics Software Engineer Nanodegree
Related Education Paths
Notable People in This Field
- Sebastian Thrun
- Andrew Ng
Related Books
Description
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.Outline
- Learn 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. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time. Learn to fuse data from three of the primary sensors that robots use: lidar, camera, and radar.
Summary of User Reviews
The Sensor Fusion Engineer Nanodegree program offered by Udacity has received positive reviews from students. They praise the course for its hands-on approach to learning and the practical skills they acquired.Key Aspect Users Liked About This Course
Hands-on approach to learningPros from User Reviews
- Practical skills acquired
- Experienced and knowledgeable instructors
- Real-world projects that challenge students
- Flexible schedule allows for self-paced learning
Cons from User Reviews
- Some technical issues with the platform
- High cost compared to other online courses
- Limited interaction with other students
Self Driving Car Engineer
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As part of their capsto Self-driving cars are set to revolutionize the way we live. Then, you'll work with a team to program Carla, Udacity’s real self-driving car....
Sensor Fusion Engineer
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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....
State Estimation and Localization for Self-Driving Cars
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Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. By the end of this course, you will be able to:...
