Fast Sensor Scheduling for Spatially Distributed Sensors

Arai, Shigeo; Iwatani, Yasushi; Hashimoto, Koichi

doi:10.1109/tac.2011.2141450

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…points show points whose Feature Indexes are the same. In figs.6(a)-(e), different dimensions of the compressed 3D feature are set and the maximum numbers of Feature Index are 3 4 , 4 4 , 5 4 , 6 4 , and 7 4 , respectively, where n d = 4 in eq. (20).…”

Section: Numerical Experimentsmentioning

confidence: 99%

FAst Detection Algorithm for 3D Keypoints (FADA-3K)

2020

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3D keypoint is widely used for object recognition and pose estimation with a 3D point cloud since it has robustness against measurement noise and occlusion. Many sets of 3D keypoints' definitions and the corresponding detection methods of 3D keypoints have been proposed. It is essentially important to select suitable 3D keypoint depending on a target object, scene situation, and 3D measurement system. While there have been a lot of useful methods for detecting 3D keypoints, detecting time is an issue, especially for real-time applications, such as robot vision. The detecting of 3D keypoints tends to entail a trade-off between computation time and the robustness of the 3D keypoint. To solve these problems, we propose a FAst Detection Algorithm for 3D keypoints named FADA-3K. A user can select one of the suitable 3D keypoints and the corresponding detection method which has been already proposed since FADA-3K can be implemented as an add-on of the existing detection methods. Numerical experiments show that FADA-3K can achieve about nine times faster detection than conventional approaches to detecting 3D keypoints. INDEX TERMS 3D keypoint, point cloud processing, robot vision, robotic bin-picking.

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Section: Numerical Experimentsmentioning

confidence: 99%

FAst Detection Algorithm for 3D Keypoints (FADA-3K)

2020

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“…Moreover, in most cases the actual optimal period for the schedule is unknown and depends on the particular problem setting. Other efficient approaches where proposed in [31,32] which do not require the periodic schedule assumption. Nonetheless, the actual form of the scheduling policies in the previous works is tightly coupled to the particular structure of the systems under consideration and the cost function.…”

Section: Introductionmentioning

confidence: 99%

PLATE: A perception-latency aware estimator

2023

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“…These methods require 3D models of target objects and 6D pose estimation of the objects for robotic bin-picking. With the development of technology of three-dimensional measurements [12][13][14][15], a lot of techniques for pose estimation algorithms with 3D point cloud have been proposed, such as 3D feature [7,[16][17][18][19], 3D keypoint detection [20,21], segmentation [22,23], and Iterative Closest Point (ICP) [24], have been proposed. With the perfect knowledge of the object's 6D pose, the robot can grasp the object with pre-designed grasp configurations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Fast Grasp Planning for Robotic Bin-picking by Small Data Set without GPU

Arai¹,

Zhao²,

Purnomo³

et al. 2021

Preprint

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<div>This paper proposes a deep learning-based fast grasp detection method with a small dataset for robotic bin-picking. We consider the problem of grasping stacked up mechanical parts on a planar workspace using a parallel gripper. In this paper, we use a deep neural network to solve the problem with a single depth image. To reduce the computation time, we propose an edge-based algorithm to generate potential grasps. Then, a convolutional neural network (CNN) is applied to evaluate the robustness of all potential grasps for bin-picking. Finally, the proposed method ranks them and the object is grasped by using the grasp with the highest score. In bin-picking experiments, we evaluate the proposed method with a 7-DOF manipulator using textureless mechanical parts with complex shapes. The success ratio of grasping is 97%, and the average computation time of CNN inference is less than 0.23[s] on a laptop PC without a GPU array. In addition, we also confirm that the proposed method can be applied to unseen objects which are not included in the training dataset. </div>

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Fast Sensor Scheduling for Spatially Distributed Sensors

Cited by 16 publications

References 15 publications

FAst Detection Algorithm for 3D Keypoints (FADA-3K)

FAst Detection Algorithm for 3D Keypoints (FADA-3K)

PLATE: A perception-latency aware estimator

Deep Learning-based Fast Grasp Planning for Robotic Bin-picking by Small Data Set without GPU

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