전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
권호 표지
DOI QR코드

DOI QR Code

Advanced Air Mobility를 위한 영상 기반 위치 추정 및 Geo-Referencing 기술 동향

A Survey on Vision-based Localization and Geo-Referencing Technology for Advanced Air Mobility

  • 최의환 (모빌리티인프라연구실) ;
  • 이대규 (모빌리티인프라연구실) ;
  • 위현중 (모빌리티인프라연구실) ;
  • 주인학 (모빌리티인프라연구실) ;
  • 장인성 (모빌리티인프라연구실)
  • U. Choi ;
  • D. Lee ;
  • H. Wi ;
  • I. Joo ;
  • I. Jang
  • 발행 : 2024.08.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

As Advanced Air Mobility (AAM) technologies evolve, ensuring accurate navigation and localization in complex urban airspaces has become crucial. Because the Global Navigation Satellite System (GNSS) is prone to vulnerabilities in urban flight environment, an alternative localization technique is required. This paper examines vision-based localization technologies to enhance GNSS-free navigation. In addition, we explore various geo-referencing studies that utilize pre-existing spatial databases to improve the accuracy of vision-based localization under GNSS-denied conditions. This paper discusses the various types of onboard vision camera sensors, vision-based localization, spatial information databases, feature extraction methods, and matching techniques that contribute to the development of a vision-based localization and geo-referencing system for AAM, ensuring safety and reliability in urban operations.

키워드

과제정보

본 연구는 2024년도 ETRI(한국전자통신연구원)의 재원으로 DNA 기반 국가 지능화 핵심 기술 사업[No. 24ZR1210]의 지원을 받아 수행되었습니다.

참고문헌

  1. V. Stepanyan et al., "Adaptive multi-sensor information fusion for autonomous urban air mobility operations," in Proc. AIAA Scitech Forum, (Virtual Event), Jan. 2021.
  2. 국토교통과학기술진흥원, "한국형 도심항공교통(K-UAM) 기술로드맵," 2021.
  3. C.A. Ippolito et al., "Concepts for distributed sensing and collaborative airspace autonomy in advanced urban air mobility," in Proc. AIAA Scitech Forum, (Oxon Hill, MD, USA), Jan. 2023.
  4. K.H. Shish et al., "Survey of capabilities and gaps in external perception sensors for autonomous urban air mobility applications," in Proc. AIAA Scitech Forum, (Virtual Event), Jan. 2021.
  5. A. Couturier and M.A. Akhloufi, "A review on absolute visual localization for UAV," Robot. Auton. Syst., vol. 135, 2021.
  6. A. Yol and Delabarre, "Vision-based absolute localization for unmanned aerial vehicles," in Proc. IEEE/RSJ IROS, (Chicago, IL, USA), Sept. 2014, pp. 3429-3434.
  7. D. Lee et al., "Enhancing state estimator for autonomous racing: Leveraging multi-modal system and managing computing resources," IEEE Trans. Intell. Veh., 2024.
  8. D. Nister, O. Naroditsky, and J. Bergen, "Visual odometry," in Proc. CVPR, (Washington, DC, USA), June 2004.
  9. Y. Cheng et al., "Visual odometry on the mars exploration rovers," in Proc. IEEE SMC, (Waikoloa, HI, USA), Oct. 2005.
  10. M. Aqel et al., "Review of visual odometry: Types, approaches, challenges, and applications," SpringerPlus, vol. 5, 2016, pp. 1-26.
  11. C. Forster et al., "On-manifold preintegration for real-time visual-inertial odometry," IEEE Trans. Robot., vol. 33, no. 1, 2016, pp. 1-21.
  12. T. Taketomi, H. Uchiyama, and S. Ikeda, "Visual SLAM algorithms: A survey from 2010 to 2016," IPSJ Trans. Comput. Vis. Appl., vol. 9, 2017, pp. 1-11.
  13. I.A. Kazerouni et al., "A survey of state-of-the-art on visual SLAM," Expert Syst. Appl., vol. 205, 2022, article no. 117734.
  14. R. Acuna and V. Willert, "Dynamic markers: UAV landing proof of concept," in Proc. LARS/SBR/WRE, (Joao Pessoa, Brazil), Nov. 2018.
  15. P.H. Nguyen et al., "Remote marker-based tracking for UAV landing using visible-light camera sensor," Sensors, vol. 17, no. 9, 2017.
  16. OpenStreetMap Foundation, https://openstreetmap.org
  17. 공간정보산업진흥원, 대국민 지도서비스 브이월드, https://www.vworld.kr
  18. 국토지리정보원, 국토정보 서비스, https://www.ngii.go.kr/kor/content.do?sq=237
  19. 환경부, 환경공간정보서비스-토지피복지도 맵 서비스-'개요' 탭, https://egis.me.go.kr/intro/land.do
  20. J.H. Kim et al., "3D viewer development to support 3D building modeling," J. Korean Soc. Geospat. Inf. Sci., vol. 29, no. 4, pp. 47-53.
  21. 환경부, 환경공간정보서비스-토지피복지도 맵 서비스-'MAP 소개' 탭, https://egis.me.go.kr/intro/land.do
  22. S. Jiang et al., "Unmanned aerial vehicle-based photogrammetric 3D mapping: A survey of techniques, applications, and challenges," IEEE Geosci. Remote Sens. Mag., vol. 10, no. 2, 2021, pp. 135-171.
  23. M.P.S. Tondewad and M.M.P. Dale, "Remote sensing image registration methodology: Review and discussion," Procedia Comput. Sci., vol. 171, 2020, pp. 2390-2399.
  24. S. Karim et al., "Comparative analysis of feature extraction methods in satellite imagery," J. Appl. Remote Sens., vol. 11, no. 4, 2017.
  25. P.-E. Sarlin et al., "Superglue: Learning feature matching with graph neural networks," in Proc. IEEE/CVF. CVPR, (Virtual), June 2020.
  26. J. Edstedt et al., "DeDoDe: Detect, don't describe-describe, don't detect for local feature matching," arXiv preprint, CoRR, 2024, arXiv: 2308.08479.
  27. J. Johnson, M. Douze, and H. Jegou, "Billion-scale similarity search with GPUs," IEEE Trans. Big Data, vol. 7, no. 3, 2019, pp. 535-547.
  28. M. Douze et al., "The faiss library," arXiv preprint, CoRR, 2024, arXiv: 2401.08281.
  29. M. Schleiss, "Translating aerial images into street-map-like representations for visual self-localization of UAVs," in Proc. ISPRS XLII-2, (Enschede, Netherlands), June 2019, pp. 575-580.
  30. H.M. Ali, A. Boshir, and I.M. Ariful, "Automatic extractions of road intersections from satellite imagery in urban areas," in Proc. ICECE, (Dhaka, Bangladesh), Dec. 2010, pp. 686-689.
  31. T. Wang, K. Celik, and A.K. Somani, "Characterization of mountain drainage patterns for GPS-denied UAS navigation augmentation," Mach. Vis. Appl., vol. 27, 2016, pp. 87-101.
  32. A. Masselli, R. Hanten, and A. Zell, "Localization of unmanned aerial vehicles using terrain classification from aerial images," in Intelligent Autonomous Systems 13, vol. 302, Springer, 2016, pp. 831-842.
  33. Z. Jin et al., "Multi-region scene matching based localisation for autonomous vision navigation of UAVs," J. Navig., vol. 69, no. 6, 2016, pp. 1215-1233.
  34. A. Nassar et al., "A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization," in Proc. CVPR, (Salt Lake City, UT, USA), June 2018, pp. 1513-1523.
  35. M. Bianchi and T.D. Barfoot, "UAV localization using autoencoded satellite images," IEEE Robot. Autom. Lett., vol. 6, no. 2, 2021, pp. 1761-1768.
  36. A.P. Tanchenko et al., "UAV navigation system Autonomous correction algorithm based on road and river network recognition," Gyrosc. Navig., vol. 11, no. 4, pp. 293-299.
  37. K. Hong et al., "Particle filters using gaussian mixture models for vision-based navigation," J. Korean Soc. Aeronaut. Space Sci., vol. 47, no. 4, 2019, pp. 274-282.
  38. K. Hong et al., "Vision-based navigation using gaussian mixture model of terrain features," in Proc. AIAA Scitech Forum, (Orlando, FL, USA), Jan. 2020.
  39. K. Hong et al., "Particle filter approach to vision-based navigation with aerial image segmentation," J. Aerosp. Inf. Syst., vol. 18, no. 12, 2021, pp. 964-972.