The Sensor Fusion Engineer Nanodegree program consists of four courses that teach the fundamentals of sensor fusion and perception for self-driving cars. The program covers lidar, radar, camera, and Kalman filters, and includes lessons on working with real-world data, filtering, segmentation, clustering, and object tracking. In addition, students will work on a capstone project that involves building a complete sensor fusion pipeline for autonomous vehicles. Upon completing the program, graduates will have the skills and knowledge necessary to design and implement sensor fusion systems for self-driving cars.
Overview
Syllabus
- Welcome
- Lidar Obstacle Detection
- Camera
- Radar
- Kalman Filters
- Career Services
- These Career Services will ensure you make meaningful connections with industry professionals to accelerate your career growth - whether looking for a job or opportunities to collaborate with your peers. Unlike your Nanodegree projects, you do not need to meet specifications on these Services to progress in your program. Submit these Career Services once, and get honest, personalized feedback and next steps from Udacity Career Coaches!
- Autonomous Systems Interview
- Start off with some tips on interviewing for an autonomous systems role, then watch how candidates approach their interview questions. Finish off by practicing some questions of your own!
- Appendix
Taught by
David Silver, Stephen Welch, Andreas Haja, Abdullah Zaidi and Aaron Brown
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Reviews
4.3 rating, based on 3 Class Central reviews
4.8 rating at Udacity based on 175 ratings
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Very interesting and relevant content. The C++ programming is a bit more advanced than expected. I did the Intro to Self Driving Cars Nanodegree in advance, but still feel I lack some C++ skills. Resulting in that I spend longer time on programming tasks. But I learn C++ from this, which is nice.
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Generally enjoyed the course but found some other Udacity courses better designed. The first couple of sections were in my opinion too focused on using a particular library instead of teaching the fundamentals. The computer vision projects also required a lot of manual work that could have been removed with a better organization of the project's initial code. The last section on the Unscented Kalman Filter was however very good.
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Nikhil NairThe program is well thought out, the content is remarkably close to the real world. The projects and examples provide a good balance of hands-on learning and theory.
