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To enable autonomous robotic manipulation in unstructured environments, wepresent SegICP-DSR, a real- time, dense, semantic scene reconstruction and poseestimation algorithm that achieves mm-level pose accuracy and standarddeviation (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 a29% increase in accuracy, and a 14% increase in success rate compared to SegICPin cluttered, unstruc- tured environments. The performance increase ofSegICP-DSR arises from (1) improved deep semantic segmentation underadversarial training, (2) precise automated calibration of the camera intrinsicand extrinsic parameters, (3) viewpoint specific ray-casting of the modelgeometry, and (4) dense semantic ElasticFusion point clouds for registration.We benchmark the performance of SegICP-DSR on thousands of pose-annotated videoframes and demonstrate its accuracy and efficacy on two tight tolerancegrasping and insertion tasks using a KUKA LBR iiwa robotic arm.

SegICP-DSR: Dense Semantic Scene Reconstruction and Registration