COMPARISON OF THE GRAPH-OPTIMIZATION FRAMEWORKS G2O AND SBA
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This thesis starts with an introduction to Simulataneous Localization and Mapping (SLAM) and more background on Visual SLAM (VSLAM). The goal of VSLAM is to map the world with a camera, and at the same time localize the camera in that world. One important step is to optimize the acquired map, which can be done in several diﬀerent ways. In this thesis, two state-of-the-art optimization algorithms are identiﬁed and compared, namely the g2o package and the SBA package. The results show that SBA is better on smaller datasets, and g2o on larger. It is also discovered that there is an error in the implementation of the pinhole camera model in the SBA package.
Place, publisher, year, edition, pages
2016. , 25 p.
SLAM, g2o, SBA, graph-optimization, new college, venice, mono-slam, VSLAM
IdentifiersURN: urn:nbn:se:mdh:diva-31764OAI: oai:DiVA.org:mdh-31764DiVA: diva2:934179
Subject / course
Ahlberg, Carl, Doctoral StudentSpampinato, Giacomo, Senior LecturerHolmberg, Björn, PhD
Ekström, Mikael, Associate Professor