PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

PHD Thesis - Institute for Computer Graphics and Vision - Graz ...


Visual map building and localization for mobile robots is a wide spread field of research. Research

done so far has already produced a vast variety of different approaches, yet key questions are

still open. In this work we present novel approaches focussing on visual map building and

global localization. First we propose a piece-wise planar world representation which uses small

planar patches as landmarks. The new world representation is designed to ease the landmark

correspondence problem. The map is augmented with the original appearances of the landmarks

and invariant descriptors combining geometry and appearance based features in a local approach.

For the building of the piece-wise planar map we make use of the recent advances in widebaseline

stereo matching by using local detectors. The current state-of-the-art of local detectors

is revised in this work and a new method to evaluate the performance of the different methods is

proposed. Based on the evaluation results new methods for wide-baseline region matching and

piece-wise planar scene reconstruction are presented. A map building algorithm is presented,

which creates a piece-wise planar world map where the world map consists of a set of linked

metric sub-maps. Second a novel algorithm for global localization from a single small landmark

match is presented. The method produces an accurate 6DOF pose estimate, gaining benefits

from the piece-wise planar world representation. Accurate pose estimation from a single small

landmark makes the localization very robust even against large occlusions. Map building and

localization are experimentally evaluated on two indoor scenarios. Map building and localization

prove to be competitive to other state-of-the-art approaches. In fact, the localization accuracy

is competitive to recent approaches, but gets computed from a single landmark match only. The

experimental results successfully demonstrate the benefits and strengths of our novel approach.


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