  Creating and maintaining an accurate representation of the environment is an
essential capability for every service robot. Especially for household robots
acting in indoor environments, semantic information is important. In this
paper, we present a semantic mapping framework with modular map
representations. Our system is capable of online mapping and object updating
given object detections from RGB-D data and provides various 2D and
3D~representations of the mapped objects. To undo wrong data associations, we
perform a refinement step when updating object shapes. Furthermore, we maintain
an existence likelihood for each object to deal with false positive and false
negative detections and keep the map updated. Our mapping system is highly
efficient and achieves a run time of more than 10 Hz. We evaluated our approach
in various environments using two different robots, i.e., a Toyota HSR and a
Fraunhofer Care-O-Bot-4. As the experimental results demonstrate, our system is
able to generate maps that are close to the ground truth and outperforms an
existing approach in terms of intersection over union, different distance
metrics, and the number of correct object mappings
