Where: Room 203/204
Start Time: 16:20
End Time: 17:25
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 we introduce the fundamentals of monocular SLAM algorithms, from input images to 3D maps. We take 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. We also discuss implementation considerations for these components, including arithmetic precision required to achieve acceptable mapping and tracking accuracy.