본문 바로가기
KERD
논문KCI 등재

IMU 센서와 비전 시스템을 활용한 달 탐사 로버의 위치추정 알고리즘

Localization Algorithm for Lunar Rover using IMU Sensor and Vision System

1

조회

0

다운로드

2

인용

0

참고문헌

키워드

초록

In this paper, we propose an algorithm that estimates the location of lunar rover using IMU and vision system instead of the dead-reckoning method using IMU and encoder, which is difficult to estimate the exact distance due to the accumulated error and slip. First, in the lunar environment, magnetic fields are not uniform, unlike the Earth, so only acceleration and gyro sensor data were used for the localization. These data were applied to extended kalman filter to estimate Roll, Pitch, Yaw Euler angles of the exploration rover. Also, the lunar module has special color which can not be seen in the lunar environment. Therefore, the lunar module were correctly recognized by applying the HSV color filter to the stereo image taken by lunar rover. Then, the distance between the exploration rover and the lunar module was estimated through SIFT feature point matching algorithm and geometry. Finally, the estimated Euler angles and distances were used to estimate the current position of the rover from the lunar module. The performance of the proposed algorithm was been compared to the conventional algorithm to show the superiority of the proposed algorithm.

Abstract

In this paper, we propose an algorithm that estimates the location of lunar rover using IMU and vision system instead of the dead-reckoning method using IMU and encoder, which is difficult to estimate the exact distance due to the accumulated error and slip. First, in the lunar environment, magnetic fields are not uniform, unlike the Earth, so only acceleration and gyro sensor data were used for the localization. These data were applied to extended kalman filter to estimate Roll, Pitch, Yaw Euler angles of the exploration rover. Also, the lunar module has special color which can not be seen in the lunar environment. Therefore, the lunar module were correctly recognized by applying the HSV color filter to the stereo image taken by lunar rover. Then, the distance between the exploration rover and the lunar module was estimated through SIFT feature point matching algorithm and geometry. Finally, the estimated Euler angles and distances were used to estimate the current position of the rover from the lunar module. The performance of the proposed algorithm was been compared to the conventional algorithm to show the superiority of the proposed algorithm.

참고문헌 (16)

  1. Y. Zheng, "China's Lunar Exploration Program : Present and future", Planetary and Space Science, 56(7), pp.881~886 (2008)
  2. H. -S. Jeong, "Study on Estimation of Optimal Slip ratio for Maximum Drawbar pull of Exploration Rovers", Institute of Electronics Engineers of Korea Summer General Conference, pp.1164~1166 (2016)
  3. W. -S. Eom, "Development Trend of Unmanned Planetary Exploration Rover by NASA", Current Industrial and Technological Trends in Aerospace, 10(2), pp.102~111 (2012)
  4. P. Kim, "Kalman Filter for Beginners with MATLAB Examples", CreateSpace Independent Publishing Platform, 161~191 (2011)
  5. 박영식, "융합된 다중 센서와 EKF 기반의 무인잠수정의 항법시스템 설계", 제어.로봇.시스템학회 논문지, 22(7), pp.562~569 (2016), 제어·로봇·시스템학회[DOI]
  6. Y. Kim, "Robust Navigational system for a Transporter Using GPS", INS Fusion, 2018, pp.65(4), 3346~3354
  7. M. Maimone, "Two Years of Visual Odometry on the Mars Exploration Rovers", Journal of Field Robotics, 24(3), pp.169~186 (2007)
  8. N. -R. Jung, "Automatical Middle Ear Disease Judgement using HSV Color Space", Institute of Electronics Engineers of Korea Summer General Conference, pp.801~803 (2015)
  9. V. A. Oliveira, "Skin Detection using HSV color space", Workshops of Sibgrapi, pp.1~2 (2009)
  10. C. Harris, "A combined corner and edge detector", Alvey Vision Conference, pp.23.1~23.6 (1988)
  11. D. G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer vision, 60(2), pp.91~110 (2004)
  12. H. Bay, "Speeded-Up Robust Features(SURF)", Computer vision and Image Understanding, 110(3), pp.346~359 (2008)
  13. G. Yu, "ASIFT: An Algorithm for Fully Affine Invariant Comparison", Image Processing On- Line, 1, pp.11~38 (2011)
  14. 김용민, "증강현실에서 가려진 마커를 위한 Affine-SIFT 정합 점들을 이용한 마커 검출 기법", 전자공학회논문지 - CI, 48(2), pp.55~65 (2011), 대한전자공학회
  15. I. -H. Oh, "A robust technique for extraction of correspondences from uncalibrated images using epipolar geometry", HCI Korea 2013, 2, pp.416~420 (2003)
  16. H. -S. Lee, "Depth Information Recognition In 3D Space Using Stereo vision", The Korea Entertainment Industry Association Fall Conference, pp.121~124 (2009)

이 간행물의 다른 논문 (9건)