Konrad, Stepan (2014)
Pose Estimation and Loop Closing from Video Data.
Technische Universität Darmstadt
Bachelorarbeit, Bibliographie

Kurzbeschreibung (Abstract)

In robotics the simultaneous localisation and mapping (SLAM) algorithms are a well studied approach to estimate the position of a robot vehicle while creating a map of the surrounding. The majority of these algorithms use odometry or GPS sensors to cope with large outdoor trajectories. From a similar point of view the computer vision community uses structure from motion (SfM) algorithms to estimate accurate camera poses of an unconstrained image data set. In the past few years the video resolution of consumer cameras has reached a level where it becomes attractive for research purposes as input to these algorithms. The goal of this thesis is to adapt an SfM approach to use this video data. However there are two main problems: The approach has to handle a large number of input frames efficiently while still detecting similar previously seen locations (loops) of the input data without performing an exhaustive matching of all image pairs. This thesis presents an approach using a vocabulary tree guided matching scheme which solves this problem. Performance is compared to exhaustive matching on different input scenes. However, this is still not sufficient to reconstruct large datasets that contain loop closures in the camera path. Due to the incremental manner of the majority of SfM algorithms, drifts occur during the estimation of camera poses. In this thesis different solutions to this problems are discussed. One specific solution using a global bundle adjustment with additional loop closing constraints is demonstrated on a large outdoor scene containing multiple loops.

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