  Recent Semantic SLAM methods combine classical geometry-based estimation with
deep learning-based object detection or semantic segmentation. In this paper we
evaluate the quality of semantic maps generated by state-of-the-art class- and
instance-aware dense semantic SLAM algorithms whose codes are publicly
available and explore the impacts both semantic segmentation and pose
estimation have on the quality of semantic maps. We obtain these results by
providing algorithms with ground-truth pose and/or semantic segmentation data
available from simulated environments. We establish that semantic segmentation
is the largest source of error through our experiments, dropping mAP and OMQ
performance by up to 74.3% and 71.3% respectively.

Robotics