Motion Vision
Segmenting the environment surrounding an autonomous vehicle into coherently moving regions is a vital first step towards intelligent autonomous navigation. Without this temporal information, navigation becomes a simple obstacle avoidance scheme that is inappropriate in highly dynamic environments such as roadways and places where many people congregate. The book begins by looking at the problem of motion estimation from biological, algorithmic and digital perspectives. It goes on to describe an algorithm that fits with the motion processing model, and hardware and software constraints. This algorithm is based on the optical flow constraint equation and introduces range information to resolve the depth-velocity ambiguity, which is critical for autonomous navigation. Finally, implementation of the algorithm in digital hardware is described in detail, covering both the initial motion processing model and the chosen hardware platforms, and the global functional structure of the system.
Available editions
Save this book
Save
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
