TY - GEN
T1 - General object tip detection and pose estimation for robot manipulation
AU - Shukla, Dadhichi
AU - Erkent, Özgür
AU - Piater, Justus
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Robot manipulation tasks like inserting screws and pegs into a hole or automatic screwing require precise tip pose estimation. We propose a novel method to detect and estimate the tip of elongated objects. We demonstrate that our method can estimate tip pose to millimeterlevel accuracy. We adopt a probabilistic, appearance-based object detection framework to detect pegs and bits for electric screw drivers. Screws are difficult to detect with feature-or appearance-based methods due to their reflective characteristics. To overcome this we propose a novel adaptation of RANSAC with a parallel-line model. Subsequently, we employ image moments to detect the tip and its pose. We show that the proposed method allows a robot to perform object insertion with only two pairs of orthogonal views, without visual servoing.
AB - Robot manipulation tasks like inserting screws and pegs into a hole or automatic screwing require precise tip pose estimation. We propose a novel method to detect and estimate the tip of elongated objects. We demonstrate that our method can estimate tip pose to millimeterlevel accuracy. We adopt a probabilistic, appearance-based object detection framework to detect pegs and bits for electric screw drivers. Screws are difficult to detect with feature-or appearance-based methods due to their reflective characteristics. To overcome this we propose a novel adaptation of RANSAC with a parallel-line model. Subsequently, we employ image moments to detect the tip and its pose. We show that the proposed method allows a robot to perform object insertion with only two pairs of orthogonal views, without visual servoing.
KW - Peg-in-hole insertion
KW - Pose estimation
KW - Tool tip detection
UR - https://www.scopus.com/pages/publications/84948968549
U2 - 10.1007/978-3-319-20904-3_33
DO - 10.1007/978-3-319-20904-3_33
M3 - Conference contribution
AN - SCOPUS:84948968549
SN - 9783319209036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 364
EP - 374
BT - Computer Vision Systems - 10th International Conference, ICVS 2015, Proceedings
A2 - Gasteratos, Antonios
A2 - Nalpantidis, Lazaros
A2 - Kruger, Volker
A2 - Eklundh, Jan-Olof
PB - Springer Verlag
T2 - 10th International Conference on Computer Vision Systems, ICVS 2015
Y2 - 6 July 2015 through 9 July 2015
ER -