Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
This thesis presents a study of the signed distance function as a three-dimensional
implicit surface representation and provides a detailed overview of its
different properties. A method for generating such a representation using the
depth-image output from a Kinect camera is reviewed in detail. In order to improve
the quality of the implicit function that can be obtained, registration of
multiple sensor views is proposed and formulated as a camera pose-estimation
To solve this problem, we first propose to minimize an objective function,
based on the signed distance function itself. We then linearise this objective
and reformulate the pose-estimation problem as a sequence of convex optimization
problems. This allows us to combine multiple depth measurements
into a single distance function and perform tracking using the resulting surface
Having these components well defined and implemented in a multi-threaded
fashion, we tackle the problem of object detection. This is done by applying the
same pose-estimation procedure to a 3D object template, at several locations,
in an environment reconstructed using the aforementioned surface representation.
We then present results for localization, mapping and object detection.
Experiments on a well-known benchmark indicate that our method for localization
performs very well, and is comparable both in terms of speed and
error to similar algorithms that are widely used today. The quality of our surface
reconstruction is close to the state of the art. Furthermore, we show an
experimental set-up, in which the location of a known object is successfully
determined within an environment, by means of registration.
2012. , 60 p.