6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping

• URL: http://arxiv.org/abs/2111.06276v1
• Date: Thu, 11 Nov 2021 15:36:55 GMT
• Title: 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
• Authors: Tuan-Tang Le, Trung-Son Le, Yu-Ru Chen, Joel Vidal, Chyi-Yeu Lin
• Abstract summary: Real-time robotic grasping is a priority target for highly advanced autonomous systems. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy.
• Score: 0.19686770963118383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/.

Related papers

• Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation [68.75387874066647]
  We propose an Uncertainty-Aware testing-time optimization framework for 3D human pose estimation. Our approach outperforms the previous best result by a large margin of 4.5% on Human3.6M.
  arXiv  Detail & Related papers  (2024-02-04T04:28:02Z)
• Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary? [6.8067583993953775]
  We develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy. The presented system achieves competitive accuracy to the state-of-the-art approaches with only 23 ms computing, showing it is a suitable candidate for deploying to real car-hold applications.
  arXiv  Detail & Related papers  (2022-06-28T09:53:00Z)
• Unseen Object 6D Pose Estimation: A Benchmark and Baselines [62.8809734237213]
  We propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently.
  arXiv  Detail & Related papers  (2022-06-23T16:29:53Z)
• MPF6D: Masked Pyramid Fusion 6D Pose Estimation [1.2891210250935146]
  We present a new method to estimate the 6D pose of objects that improves upon the accuracy of current proposals. Our method can be used in real-time with its low inference time of 0.12 seconds and has high accuracy.
  arXiv  Detail & Related papers  (2021-11-17T20:23:54Z)
• Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation [87.54604263202941]
  We propose a tiny deep neural network of which partial layers are iteratively exploited for refining its previous estimations. We employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
  arXiv  Detail & Related papers  (2021-11-11T23:31:34Z)
• SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation [81.03485688525133]
  We propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE) Specifically, in the training process, we enable SIMPLE to mimic the pose knowledge from the high-performance top-down pipeline. Besides, SIMPLE formulates human detection and pose estimation as a unified point learning framework to complement each other in single-network.
  arXiv  Detail & Related papers  (2021-04-06T13:12:51Z)
• PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
  Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space. We propose a model that unifies these two tasks in the same metric space. Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
  arXiv  Detail & Related papers  (2021-01-17T05:11:38Z)
• Fast Uncertainty Quantification for Deep Object Pose Estimation [91.09217713805337]
  Deep learning-based object pose estimators are often unreliable and overconfident. In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation.
  arXiv  Detail & Related papers  (2020-11-16T06:51:55Z)
• se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains [12.71983073907091]
  This work proposes a data-driven optimization approach for long-term, 6D pose tracking. It aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object's model. The proposed approach achieves consistently robust estimates and outperforms alternatives, even though they have been trained with real images.
  arXiv  Detail & Related papers  (2020-07-27T21:09:36Z)
• Single Shot 6D Object Pose Estimation [11.37625512264302]
  We introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. A fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously.
  arXiv  Detail & Related papers  (2020-04-27T11:59:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.