To Örebro University

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

problem.

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

representation.

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.

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