Bookmark and Share

"Fundamentals of Monocular SLAM," a Presentation from Cadence

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Shrinivas Gadkari, Design Engineering Director at Cadence, presents the "Fundamentals of Monocular SLAM" tutorial at the May 2019 Embedded Vision Summit.

Simultaneous Localization and Mapping (SLAM) refers to a class of algorithms that enables a device with one or more cameras and/or other sensors to create an accurate map of its surroundings, to determine the device’s location relative to its surroundings and to track its path as it moves through this environment. This is a key capability for many new use cases and applications, especially in the domains of augmented reality, virtual reality and mobile robots.

Monocular SLAM is a type of SLAM that relies exclusively on a monocular image sequence captured by a moving camera. In this talk, Gadkari introduces the fundamentals of monocular SLAM algorithms, from input images to 3D map. He takes a close look at key components of monocular SLAM algorithms, including Oriented Fast and Oriented Brief (ORB), Fundamental Matrix-based Pose Estimation, stitching together poses using translation estimation and loop closure. He also discusses implementation considerations for these components, including arithmetic precision required to achieve acceptable mapping and tracking accuracy.