  To enable autonomous robotic manipulation in unstructured environments, we
present SegICP-DSR, a real- time, dense, semantic scene reconstruction and pose
estimation algorithm that achieves mm-level pose accuracy and standard
deviation (7.9 mm, {\sigma}=7.6 mm and 1.7 deg, {\sigma}=0.7 deg) and suc-
cessfully identified the object pose in 97% of test cases. This represents a
29% increase in accuracy, and a 14% increase in success rate compared to SegICP
in cluttered, unstruc- tured environments. The performance increase of
SegICP-DSR arises from (1) improved deep semantic segmentation under
adversarial training, (2) precise automated calibration of the camera intrinsic
and extrinsic parameters, (3) viewpoint specific ray-casting of the model
geometry, and (4) dense semantic ElasticFusion point clouds for registration.
We benchmark the performance of SegICP-DSR on thousands of pose-annotated video
frames and demonstrate its accuracy and efficacy on two tight tolerance
grasping and insertion tasks using a KUKA LBR iiwa robotic arm.
