Improving constrained bundle adjustment through semantic scene labeling
Abstract
There is no doubt that SLAM and deep learning methods can benefit from each other. Most recent approaches to coupling those two subjects, however, either use SLAM to improve the learning process, or tend to ignore the geometric solutions that are currently used by SLAM systems. In this work, we focus on improving city-scale SLAM through the use of deep learning. More precisely, we propose to use CNNbased scene labeling to geometrically constrain bundle adjustment. Our experiments indicate a considerable increase in robustness and precision.