Details
| Original language | English |
|---|---|
| Title of host publication | 2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI) |
| Pages | 1250-1254 |
| Number of pages | 5 |
| ISBN (electronic) | 9798350378931 |
| Publication status | Published - 4 Mar 2025 |
| Event | 20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025 - Melbourne, Australia Duration: 4 Mar 2025 → 6 Mar 2025 |
Publication series
| Name | ACM/IEEE International Conference on Human-Robot Interaction |
|---|---|
| ISSN (electronic) | 2167-2148 |
Abstract
Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects enhances the program-ming experience by bridging the real and virtual environments. However, a prerequisite for pose estimation is to perform a 2D segmentation to determine the region of interest (ROI). In this work, we present an AR-based approach that enables point-and-click ROI detection through human interaction. Our proof of concept investigates how the achievable accuracy varies with the quality of the user input. The results show that the accuracy of the ROI estimation has a minimal impact on the overall accuracy. Existing limitations can be addressed by other approaches presented.
Keywords
- augmented reality, HMD, human-robot collaboration, intuitive programming, pose estimation
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 2025. p. 1250-1254 (ACM/IEEE International Conference on Human-Robot Interaction).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Point-and-Click Augmented Reality Approach Towards Pose Estimation for Robot Programming
AU - Blankemeyer, Sebastian
AU - Wendorff, David
AU - Raatz, Annika
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/3/4
Y1 - 2025/3/4
N2 - Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects enhances the program-ming experience by bridging the real and virtual environments. However, a prerequisite for pose estimation is to perform a 2D segmentation to determine the region of interest (ROI). In this work, we present an AR-based approach that enables point-and-click ROI detection through human interaction. Our proof of concept investigates how the achievable accuracy varies with the quality of the user input. The results show that the accuracy of the ROI estimation has a minimal impact on the overall accuracy. Existing limitations can be addressed by other approaches presented.
AB - Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects enhances the program-ming experience by bridging the real and virtual environments. However, a prerequisite for pose estimation is to perform a 2D segmentation to determine the region of interest (ROI). In this work, we present an AR-based approach that enables point-and-click ROI detection through human interaction. Our proof of concept investigates how the achievable accuracy varies with the quality of the user input. The results show that the accuracy of the ROI estimation has a minimal impact on the overall accuracy. Existing limitations can be addressed by other approaches presented.
KW - augmented reality
KW - HMD
KW - human-robot collaboration
KW - intuitive programming
KW - pose estimation
UR - http://www.scopus.com/inward/record.url?scp=105004879704&partnerID=8YFLogxK
U2 - 10.1109/HRI61500.2025.10974140
DO - 10.1109/HRI61500.2025.10974140
M3 - Conference contribution
AN - SCOPUS:105004879704
SN - 979-8-3503-7894-8
T3 - ACM/IEEE International Conference on Human-Robot Interaction
SP - 1250
EP - 1254
BT - 2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
T2 - 20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025
Y2 - 4 March 2025 through 6 March 2025
ER -