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What is this thesis about?

The goal of this thesis is to autonomously navigate an inexpensive, consumer grade quadcopter like the Parrot's AR.Drone to image a large scene (spread over multiplanar surfaces), and create an unrolled view. This thesis proposes methods to solve some of the problems faced with quadcopter-based explorations.

Mosaicing Scenes With Vacant Spaces

One of the major contribution of this piece of the thesis is a method to create a mosaic of a scene containing vacant spaces using a fusion of a standard vision-based stitching algorithm, with positional data captured from an IMU. Read more ...
Vacant spaces are encountered in the imaging of this scene. When individual portions are captured by a quadcopter, how does one create the complete mosaic, given that common features are unavailable? In this example, α and α' can be aligned, and so can β and β'. This is not true for the constituents of the 5-tuple (α, A, B, C, and β).

Imaging and Unrolling Multiplanar Surfaces

In many circumstances the input scene is spread over multiple planes. In such cases, we would like to image each planar region orthographically (and then create individual mosaics), and later 'unroll' the whole scene by joining the individual mosaics. In effect, we get the output mosaic of the input scene as if it were present on a single plane. Read more ...
Imaging multiplanar scenes requires change in the orientation as well as position of camera. In such cases, we output the unrolled view of the input scene.

Recognizing Fiducials Under Blur

A single quadcopter is not sufficient for imaging large multiplanar scenes due to energy constraints. Instead, one may use multiple quadcopters in collaboration for imaging multiplanar scenes. In such cases, we have to identify each quadcopter uniquely for realistic collaboration among multiple quadcopters. Read more ...
Fiducials such as ARTag can be recognized (green rectangles) on the left, but cannot be recognized (on the right) in the presence of motion blur.