Title:
Vision Based Navigation and Tracking with Small UAVs
Vision Based Navigation and Tracking with Small UAVs
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Author(s)
Beard, Randal W.
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Abstract
This talk will describe our current work on vision-based
autonomous navigation and tracking using
small UAVs and provide an overview of two ongoing
projects. The first project is relative navigation
in GPS degraded environments. There are many
applications where GPS is either restricted or
denied. We have developed an architecture that
uses a relative front end to navigate relative to key
frames, and then opportunistically uses GPS
measurements and SLAM-style loop closures in a back-end process to provide global context. We will
show some recent flight results that demonstrate
robustness to GPS failure and degradation. The
second project we will discuss is robust tracking of
multiple ground-based targets from an airborne
platform. We will present a new multiple target
tracking algorithm that is based on the random
sample consensus (RANSAC) algorithm widely used
in computer vision. A recursive version of the
RANSAC algorithm will be discussed, and its
extension to tracking multiple dynamic objects will
be explained. The performance of R-RANSAC will be
compared to state of the art target tracking
algorithms in the context of problems that are
relevant to UAV applications.
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Date Issued
2017-11-01
Extent
58:43 minutes
Resource Type
Moving Image
Resource Subtype
Lecture