한국ITS학회 논문지 (The Journal of The Korea Institute of Intelligent Transport Systems)

  • 제21권5호
  • /
  • Pages.218-232
  • /
  • 2022
  • /
  • 1738-0774(pISSN)
  • /
  • 2384-1729(eISSN)
한국ITS학회 (The Korea Institute of Intelligent Transport Systems)
권호 표지
DOI QR코드

DOI QR Code

레이더 에고 모션 추정 신뢰성 향상을 위한 도플러 속도 기반 동적 물체 추적 및 제거

Doppler Velocity-based Dynamic Object Tracking and Rejection for Increasing Reliability of Radar Ego-Motion Estimation

  • 박영상 (한국전자통신연구원 자율주행지능연구실) ;
  • 민경욱 (한국전자통신연구원 자율주행지능연구실) ;
  • 최정단 (한국전자통신연구원 지능로보틱스연구본부)
  • 투고 : 2022.08.03
  • 심사 : 2022.10.20
  • 발행 : 2022.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

차량의 물체 인식에 사용되던 센서인 레이더 센서를 위치 추정에 사용하기 위한 연구들이 진행되고 있다. 특히 레이더 센서에서 출력되는 도플러 속도를 이용하여 동적 물체와 정적 물체를 분류하고, 정적 물체만을 이용하여 에고 모션을 계산하는 방법이 연구되었다. 기존의 동적 물체 분류에서는 RANSAC을 사용한 방법이 제시되었는데, 단 한 번의 알고리즘 실패가 큰 영향을 미치는 위치 추정의 특성상 더 높은 성능을 가진 분류 방법이 필요하다. 본 논문에서는 동적 물체의 추적 및 필터링을 통해 기존 방법보다 분류 성능을 높이는 방법에 대해 제안한다. 추가적으로 GMPHD 필터를 사용하여 추적 성능을 최대로 향상시킨다. 제안된 방법은 기존의 방법과 비교하여 분류 정확도에서 더 높은 성능을 보였으며, 특히 알고리즘의 실패를 방지할 수 있다는 것을 보인다.

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

키워드

과제정보

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 21AMDP-C160548-01).

참고문헌

  1. Cen, S. H. and Newman, P.(2018), "Precise ego-motion estimation with millimeter-wave radar under diverse and challenging conditions", In 2018 IEEE International Conference on Robotics and Automation(ICRA), pp.6045-6052.
  2. Cen, S. H. and Newman, P.(2019), "Radar-only ego-motion estimation in difficult settings via graph matching", In 2019 International Conference on Robotics and Automation(ICRA), pp.298-304.
  3. Ester, M., Kriegel, H. P., Sander, J. and Xu, X.(1996), "A density-based algorithm for discovering clusters in large spatial databases with noise", ACM SIGKDD(The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining) International Conference on Knowledge Discovery & Data Mining(KDD), vol. 96, no. 34, pp.226-231.
  4. Kellner, D., Barjenbruch, M., Klappstein, J., Dickmann, J. and Dietmayer, K.(2013), "Instantaneous ego-motion estimation using doppler radar", 16th International IEEE Conference on Intelligent Transportation System(ITSC 2013), pp.869-874.
  5. Kramer, A., Stahoviak, C., Santamaria-Navarro, A., Agha-Mohammadi, A. A. and Heckman, C.(2020), "Radar-inertial ego-velocity estimation for visually degraded environments", In 2020 IEEE International Conference on Robotics and Automation(ICRA), pp.5739-5746.
  6. Lundquist, C., Hammarstrand, L. and Gustafsson, F.(2010), "Road intensity based mapping using radar measurements with a probability hypothesis density filter", IEEE Transactions on Signal Processing, vol. 59, no. 4, pp.1397-1408.
  7. Nobis, F., Shafiei, E., Karle, P., Betz, J. and Lienkamp, M.(2021), "Radar voxel fusion for 3D object detection", Applied Sciences, vol. 11, no. 12, p.5598. https://doi.org/10.3390/app11125598
  8. Park, Y. S., Shin, Y. S., Kim, J. and Kim, A.(2021), "3D ego-motion estimation using low-cost mmWave radars via radar velocity factor for pose-graph SLAM", IEEE Robotics and Automation Letters, vol. 6, no. 4, pp.7691-7698. https://doi.org/10.1109/LRA.2021.3099365
  9. Schumann, O., Hahn, M., Scheiner, N., Weishaupt, F., Tilly, J. F., Dickmann, J. and Wohler, C.(2021), "RadarScenes: A real-world radar point cloud data set for automotive applications", 2021 IEEE 24th International Conference on Information Fusion(FUSION), pp.1-8.
  10. Tilly, J. F., Haag, S., Schumann, O., Weishaupt, F., Duraisamy, B., Dickmann, J. and Fritzsche, M.(2020), "Detection and tracking on automotive radar data with deep learning", 2020 IEEE 23rd International Conference on Information Fusion(FUSION), pp.1-7.
  11. Vivet, D., Checchin, P. and Chapuis, R.(2013), "Localization and mapping using only a rotating FMCW radar sensor", Sensors, vol. 13, no. 4, pp.4527-4552. https://doi.org/10.3390/s130404527
  12. Vivet, D., Checchin, P., Chapuis, R., Faure, P., Rouveure, R. and Monod, M. O.(2012), "A mobile ground-based radar sensor for detection and tracking of moving objects", EURASIP(European Association For Signal Processing) Journal on Advances in Signal Processing, vol. 2012, no. 1, pp.1-13. https://doi.org/10.1186/1687-6180-2012-1
  13. Vo, B. N. and Ma, W. K.(2006), "The Gaussian mixture probability hypothesis density filter", IEEE Transactions on Signal Processing, vol. 54, no. 11, pp.4091-4104. https://doi.org/10.1109/TSP.2006.881190
  14. Yang, J., Ni, P., Miao, J. and Ge, H.(2022), "Improving visual multi object tracking algorithm via integrating GM-PHD and correlation filter", IET(The Institution of Engineering and Technology) Image Processing, vol. 16, no. 5, pp.1349-1358. https://doi.org/10.1049/ipr2.12413
  15. Yoon, J. H., Hwang, Y., Choi, B. and Yoon, K. J.(2016), "Fusion of local and global detectors for PHD filter-based multi-object tracking", Journal of Institute of Control, Robotics and Systems, vol. 22, no. 9, pp.773-777. https://doi.org/10.5302/J.ICROS.2016.15.0207