computer-vision-1

Robot Pose Estimation

3260 papers • 126 benchmarks • 313 datasets

Robot 6D pose estimation from single images

(Image credit: Papersgraph)

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Most implemented papers

Real-time Holistic Robot Pose Estimation with Unknown States

Shikun Ban, Juling Fan, Wentao Zhu, Xiaoxuan Ma, Yu Qiao, Yizhou Wang•Wed Feb 07 2024

This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states, employing a neural network module for each task to facilitate learning and sim-to-real transfer.

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3D Robot Pose Estimation from 2D Images

Christoph Heindl, S. Zambal, Thomas Pönitz, A. Pichler, J. Scharinger•Tue Feb 12 2019

This paper considers the task of locating articulated poses of multiple robots in images and applies staged convolutional feature detectors to 2D image inputs and computes robot instance masks using a recurrent network architecture.

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MARIO: Modular and Extensible Architecture for Computing Visual Statistics in RoboCup SPL

D. Bloisi, A. Pennisi, Cristian Zampino, Flavio Biancospino, Francesco Laus, G. D. Stefano, Michele Brienza, Rocchina Romano•Mon Sep 19 2022

This technical report describes a modular and extensible architecture for computing visual statistics in RoboCup SPL (MARIO), presented during the SPL Open Research Challenge at RoboCup 2022, held in Bangkok (Thailand). MARIO is an open-source, ready-to-use software application whose final goal is to contribute to the growth of the RoboCup SPL community. MARIO comes with a GUI that integrates multiple machine learning and computer vision based functions, including automatic camera calibration, background subtraction, homography computation, player + ball tracking and localization, NAO robot pose estimation and fall detection. MARIO has been ranked no. 1 in the Open Research Challenge.

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