Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network
Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Sensors (Basel, Switzerland) - 24(2024), 6 vom: 12. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Shengpeng [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
2D keypoints |
|---|
| Anmerkungen: |
Date Revised 30.03.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/s24061828 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370336275 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370336275 | ||
| 003 | DE-627 | ||
| 005 | 20240331002102.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240329s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/s24061828 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1357.xml |
| 035 | |a (DE-627)NLM370336275 | ||
| 035 | |a (NLM)38544091 | ||
| 035 | |a (PII)1828 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Shengpeng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Spacecraft Homography Pose Estimation with Single-Stage Deep Convolutional Neural Network |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 30.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Spacecraft pose estimation using computer vision has garnered increasing attention in research areas such as automation system theory, control theory, sensors and instruments, robot technology, and automation software. Confronted with the extreme environment of space, existing spacecraft pose estimation methods are predominantly multi-stage networks with complex operations. In this study, we propose an approach for spacecraft homography pose estimation with a single-stage deep convolutional neural network for the first time. We formulated a homomorphic geometric constraint equation for spacecraft with planar features. Additionally, we employed a single-stage 2D keypoint regression network to obtain homography 2D keypoint coordinates for spacecraft. After decomposition to obtain the rough spacecraft pose based on the homography matrix constructed according to the geometric constraint equation, a loss function based on pixel errors was employed to refine the spacecraft pose. We conducted extensive experiments using widely used spacecraft pose estimation datasets and compared our method with state-of-the-art techniques in the field to demonstrate its effectiveness | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 2D keypoints | |
| 650 | 4 | |a geometric constraint | |
| 650 | 4 | |a homography | |
| 650 | 4 | |a pose estimation | |
| 650 | 4 | |a single-stage | |
| 650 | 4 | |a spacecraft | |
| 700 | 1 | |a Yang, Wenyi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Mai, Jianting |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaohu |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Sensors (Basel, Switzerland) |d 2007 |g 24(2024), 6 vom: 12. März |w (DE-627)NLM187985170 |x 1424-8220 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2024 |g number:6 |g day:12 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/s24061828 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2024 |e 6 |b 12 |c 03 | ||