scholarly journals A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

2021 ◽  
Vol 11 (22) ◽  
pp. 10531
Author(s):  
Chenrui Wu ◽  
Long Chen ◽  
Shiqing Wu
Keyword(s):  
Pose Estimation ◽  
Real World ◽  
Graph Matching ◽  
Metric Learning ◽  
Intelligent Manufacturing ◽  
Fixed Size ◽  
Industrial Environment ◽  
Real World Applications ◽  
Dense Point ◽  
Detection Stage

6D pose estimation of objects is essential for intelligent manufacturing. Current methods mainly place emphasis on the single object’s pose estimation, which limit its use in real-world applications. In this paper, we propose a multi-instance framework of 6D pose estimation for textureless objects in an industrial environment. We use a two-stage pipeline for this purpose. In the detection stage, EfficientDet is used to detect target instances from the image. In the pose estimation stage, the cropped images are first interpolated into a fixed size, then fed into a pseudo-siamese graph matching network to calculate dense point correspondences. A modified circle loss is defined to measure the differences of positive and negative correspondences. Experiments on the antenna support demonstrate the effectiveness and advantages of our proposed method.

Generalization analysis of multi-modal metric learning

2016 ◽  
Vol 14 (04) ◽  
pp. 503-521
Author(s):  
Yunwen Lei ◽  
Yiming Ying
Keyword(s):  
Prior Knowledge ◽  
Real World ◽  
Learning Algorithms ◽  
Metric Learning ◽  
Modal Data ◽  
Rademacher Complexity ◽  
Generalization Bounds ◽  
Complexity Result ◽  
Real World Applications

Multi-modal metric learning has recently received considerable attention since many real-world applications involve multi-modal data. However, there is relatively little study on the generalization analysis of the associated learning algorithms. In this paper, we bridge this theoretical gap by deriving its generalization bounds using Rademacher complexities. In particular, we establish a general Rademacher complexity result by systematically analyzing the behavior of the resulting models with various regularizers, e.g., [Formula: see text]-regularizer on the modality level with either a mixed [Formula: see text]-norm or a Schatten norm on each modality. Our results and the discussion followed help to understand how the prior knowledge can be exploited by selecting an appropriate regularizer.

scholarly journals Crater Detection and Recognition Method for Pose Estimation

2021 ◽  
Vol 13 (17) ◽  
pp. 3467
Author(s):  
Zihao Chen ◽  
Jie Jiang
Keyword(s):  
Pose Estimation ◽  
Graph Matching ◽  
Recognition Algorithm ◽  
Matching Problem ◽  
Crater Detection ◽  
Weighted Bipartite Graph ◽  
Dense Point ◽  
Stage 1 ◽  
Two Stages ◽  
Detection And Recognition

A crater detection and recognition algorithm is the key to pose estimation based on craters. Due to the changing viewing angle and varying height, the crater is imaged as an ellipse and the scale changes in the landing camera. In this paper, a robust and efficient crater detection and recognition algorithm for fusing the information of sequence images for pose estimation is designed, which can be used in both flying in orbit around and landing phases. Our method consists of two stages: stage 1 for crater detection and stage 2 for crater recognition. In stage 1, a single-stage network with dense anchor points (dense point crater detection network, DPCDN) is conducive to dealing with multi-scale craters, especially small and dense crater scenes. The fast feature-extraction layer (FEL) of the network improves detection speed and reduces network parameters without losing accuracy. We comprehensively evaluate this method and present state-of-art detection performance on a Mars crater dataset. In stage 2, taking the encoded features and intersection over union (IOU) of craters as weights, we solve the weighted bipartite graph matching problem, which is matching craters in the image with the previously identified craters and the pre-established craters database. The former is called “frame-frame match,” or FFM, and the latter is called “frame-database match”, or FDM. Combining the FFM with FDM, the recognition speed is enabled to achieve real-time on the CPU (25 FPS) and the average recognition precision is 98.5%. Finally, the recognition result is used to estimate the pose using the perspective-n-point (PnP) algorithm and results show that the root mean square error (RMSE) of trajectories is less than 10 m and the angle error is less than 1.5 degrees.

scholarly journals Electronic Phenomena of Transition Metal Oxides

Crystals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 256
Author(s):  
Christian Rodenbücher ◽  
Kristof Szot
Keyword(s):  
Solid State ◽  
Transition Metal ◽  
Metal Oxides ◽  
Real World ◽  
Transition Metal Oxides ◽  
Major Research ◽  
Research Fields ◽  
Real World Applications

Transition metal oxides with ABO3 or BO2 structures have become one of the major research fields in solid state science, as they exhibit an impressive variety of unusual and exotic phenomena with potential for their exploitation in real-world applications [...]

scholarly journals Applications of Distributed-Order Fractional Operators: A Review

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 110
Author(s):  
Wei Ding ◽  
Sansit Patnaik ◽  
Sai Sidhardh ◽  
Fabio Semperlotti
Keyword(s):  
Fractional Calculus ◽  
Real World ◽  
Transport Processes ◽  
Model Systems ◽  
Variable Order ◽  
Fractional Operators ◽  
Research Activity ◽  
Road Map ◽  
Real World Applications ◽  
Distributed Order

Distributed-order fractional calculus (DOFC) is a rapidly emerging branch of the broader area of fractional calculus that has important and far-reaching applications for the modeling of complex systems. DOFC generalizes the intrinsic multiscale nature of constant and variable-order fractional operators opening significant opportunities to model systems whose behavior stems from the complex interplay and superposition of nonlocal and memory effects occurring over a multitude of scales. In recent years, a significant amount of studies focusing on mathematical aspects and real-world applications of DOFC have been produced. However, a systematic review of the available literature and of the state-of-the-art of DOFC as it pertains, specifically, to real-world applications is still lacking. This review article is intended to provide the reader a road map to understand the early development of DOFC and the progressive evolution and application to the modeling of complex real-world problems. The review starts by offering a brief introduction to the mathematics of DOFC, including analytical and numerical methods, and it continues providing an extensive overview of the applications of DOFC to fields like viscoelasticity, transport processes, and control theory that have seen most of the research activity to date.

scholarly journals Mobile Manipulation Hackathon: Moving into Real World Applications

2021 ◽  
pp. 2-14
Author(s):  
Maximo A. Roa ◽  
Mehmet R. Dogar ◽  
Jordi Pages ◽  
Carlos Vivas ◽  
Antonio Morales ◽  
...  
Keyword(s):  
Real World ◽  
Mobile Manipulation ◽  
Real World Applications
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