  We present a real-time semantic mapping approach for mobile vision systems
with a 2D to 3D object detection pipeline and rapid data association for
generated landmarks. Besides the semantic map enrichment the associated
detections are further introduced as semantic constraints into a simultaneous
localization and mapping (SLAM) system for pose correction purposes. This way,
we are able generate additional meaningful information that allows to achieve
higher-level tasks, while simultaneously leveraging the view-invariance of
object detections to improve the accuracy and the robustness of the odometry
estimation. We propose tracklets of locally associated object observations to
handle ambiguous and false predictions and an uncertainty-based greedy
association scheme for an accelerated processing time. Our system reaches
real-time capabilities with an average iteration duration of 65~ms and is able
to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a
public dataset. Additionally, we implemented our approach as a modular ROS
package that makes it straightforward for integration in arbitrary graph-based
SLAM methods.
