scholarly journals Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1782 ◽  
Author(s):  
◽  
Keyword(s):  
Pose Estimation ◽  
Estimation Algorithm ◽  
Feature Based

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

A novel pose estimation algorithm for robotic navigation

2015 ◽  
Vol 63 ◽  
pp. 10-21 ◽  
Author(s):  
Claudiu Pozna ◽  
Radu-Emil Precup ◽  
Péter Földesi
Keyword(s):  
Pose Estimation ◽  
Estimation Algorithm ◽  
Robotic Navigation

Pose Estimation Algorithm for Helicopter Landing Based on YOLO and PNP

2021 ◽  
pp. 3019-3028
Author(s):  
Rui Zhou ◽  
Jiayu She ◽  
Naiming Qi ◽  
Long Yu ◽  
Yanfang Liu
Keyword(s):  
Pose Estimation ◽  
Estimation Algorithm ◽  
Helicopter Landing

Pose Estimation Algorithm Based on Combined Loss Function

2019 ◽  
Vol 56 (22) ◽  
pp. 221002
Author(s):  
张德 Zhang De ◽  
李国璋 Li Guozhang ◽  
王怀光 Wang Huaiguang ◽  
张峻宁 Zhang Junning
Keyword(s):  
Loss Function ◽  
Pose Estimation ◽  
Estimation Algorithm

Tracking with a Novel Pose Estimation Algorithm

2001 ◽  
pp. 9-18 ◽  
Author(s):  
Bodo Rosenhahn ◽  
Norbert Krüger ◽  
Torge Rabsch ◽  
Gerald Sommer
Keyword(s):  
Pose Estimation ◽  
Estimation Algorithm

scholarly journals Real-Time Energy Efficient Hand Pose Estimation: A Case Study

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2828
Author(s):  
Mhd Rashed Al Koutayni ◽  
Vladimir Rybalkin ◽  
Jameel Malik ◽  
Ahmed Elhayek ◽  
Christian Weis ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Energy Efficient ◽  
Graphics Processing Units ◽  
Estimation Algorithm ◽  
High Energy ◽  
Estimation Methods ◽  
Hand Pose Estimation ◽  
Hand Pose

The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.

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