| 1. |
- Stathoulopoulos, Nikolaos, et al.
(författare)
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FRAME: Fast and Robust Autonomous 3D Point Cloud Map-Merging for Egocentric Multi-Robot Exploration
- 2023
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Ingår i: 2023 IEEE International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350323665 - 9798350323658 ; , s. 3483-3489
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Konferensbidrag (refereegranskat)abstract
- This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.
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| 2. |
- Tiger, Mattias, 1989-, et al.
(författare)
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On-Demand Multi-Agent Basket Picking for Shopping Stores
- 2023
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Ingår i: 2023 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9798350323658 - 9798350323665 ; , s. 5793-5799
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Konferensbidrag (refereegranskat)abstract
- Imagine placing an online order on your way to the grocery store, then being able to pick the collected basket upon arrival or shortly after. Likewise, imagine placing any online retail order, made ready for pickup in minutes instead of days. In order to realize such a low-latency automatic warehouse logistics system, solvers must be made to be basketaware. That is, it is more important that the full order (the basket) is picked timely and fast, than that any single item in the order is picked quickly. Current state-of-the-art methods are not basket-aware. Nor are they optimized for a positive customer experience, that is; to prioritize customers based on queue place and the difficulty associated with picking their order. An example of the latter is that it is preferable to prioritize a customer ordering a pack of diapers over a customer shopping a larger order, but only as long as the second customer has not already been waiting for too long. In this work we formalize the problem outlined, propose a new method that significantly outperforms the state-of-the-art, and present a new realistic simulated benchmark. The proposed method is demonstrated to work in an on-line and real-time setting, and to solve the on-demand multi-agent basket picking problem for automated shopping stores under realistic conditions.
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| 3. |
- Vaidis, Maxime, et al.
(författare)
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Extrinsic calibration for highly accurate trajectories reconstruction
- 2023
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Ingår i: 2023 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9798350323658 - 9798350323665 ; , s. 4185-4192
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Konferensbidrag (refereegranskat)abstract
- In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accurate and precise measurements, that are unaffected by the usual factors that deteriorate the accuracy of Global Navigation Satellite System (GNSS). While a single robotic total station can track the position of a target in three Degrees Of Freedom (DOF), three robotic total stations and three targets are necessary to yield the full six DOF pose reference. Since it is crucial to express the position of targets in a common coordinate frame, we present a novel extrinsic calibration method of multiple robotic total stations with field deployment in mind. The proposed method does not require the manual collection of ground control points during the system setup, nor does it require tedious synchronous measurement on each robotic total station. Based on extensive experimental work, we compare our approach to the classical extrinsic calibration methods used in geomatics for surveying and demonstrate that our approach brings substantial time savings during the deployment. Tested on more than 30 km of trajectories, our new method increases the precision of the extrinsic calibration by 25 % compared to the best state-of-the-art method, which is the one taking manually static ground control points.
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| 4. |
- Zimmermann, Stefanie Antonia, 1995-, et al.
(författare)
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Experimental evaluation of a method for improving experiment design in robot identification
- 2023
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Ingår i: 2023 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9798350323658 - 9798350323665 ; , s. 11432-11438
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Konferensbidrag (refereegranskat)abstract
- The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.
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| 5. |
- Chen, Yiting, et al.
(författare)
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GraspAda: Deep Grasp Adaptation through Domain Transfer
- 2023
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9798350323658 ; 2023-May
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Konferensbidrag (refereegranskat)abstract
- Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.
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| 6. |
- Dong, Hao, et al.
(författare)
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Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
- 2023
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Ingår i: Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9798350323658 ; 2023-May, s. 6189-6195
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Konferensbidrag (refereegranskat)abstract
- A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high-dimensional; 128 dimensions or even more. The higher the dimensionality, the larger the memory consumption and computational time for approaches using such descriptors. In this paper, we investigate multi-layer perceptrons (MLPs) to extract low-dimensional but high-quality descriptors. We thoroughly analyze our method in unsuper-vised, self-supervised, and supervised settings, and evaluate the dimensionality reduction results on four representative descriptors. We consider different applications, including visual localization, patch verification, image matching and retrieval. The experiments show that our lightweight MLPs trained using supervised method achieve better dimensionality reduction than PCA. The lower-dimensional descriptors generated by our approach outperform the original higher-dimensional descriptors in downstream tasks, especially for the hand-crafted ones. The code is available at https://github.com/PRBonn/descriptor-dr.
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