The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Integrated Navigation Algorithm using Velocity Incremental Vector Approach with ORB-SLAM and Inertial Measurement

속도증분벡터를 활용한 ORB-SLAM 및 관성항법 결합 알고리즘 연구

  • Kim, Yeonjo (Dept. of Aerospace Information Engineering, Konkuk University) ;
  • Son, Hyunjin (Dept. of Aerospace Information Engineering, Konkuk University) ;
  • Lee, Young Jae (Dept. of Aerospace Information Engineering, Konkuk University) ;
  • Sung, Sangkyung (Dept. of Aerospace Information Engineering, Konkuk University)
  • Received : 2018.12.04
  • Accepted : 2018.12.17
  • Published : 2019.01.01
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Abstract

In recent years, visual-inertial odometry(VIO) algorithms have been extensively studied for the indoor/urban environments because it is more robust to dynamic scenes and environment changes. In this paper, we propose loosely coupled(LC) VIO algorithm that utilizes the velocity vectors from both visual odometry(VO) and inertial measurement unit(IMU) as a filter measurement of Extended Kalman filter. Our approach improves the estimation performance of a filter without adding extra sensors while maintaining simple integration framework, which treats VO as a black box. For the VO algorithm, we employed a fundamental part of the ORB-SLAM, which uses ORB features. We performed an outdoor experiment using an RGB-D camera to evaluate the accuracy of the presented algorithm. Also, we evaluated our algorithm with the public dataset to compare with other visual navigation systems.

Keywords

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그림 1 ${\Delta}\overrightarrow{V}{^n}{_{Ref,k}}$와 ${\Delta}\overrightarrow{V}{^n}{_{INS,k}}$의 관계[11] Fig. 1 The relation between ${\Delta}\overrightarrow{V}{^n}{_{Ref,k}}$ and ${\Delta}\overrightarrow{V}{^n}{_{INS,k}}$[11]

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그림 2 V103 오일러 자세각 추정 결과 Fig. 2 Ground Truth and estimated result of Euler angles of V103

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그림 3 제안된 알고리즘과 약결합 기반 알고리즘의 V103 자세 오차 비교 Fig. 3 Attitude error of proposed and LC algorithm of V103

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그림 4 제안된 알고리즘과 약결합 기반 알고리즘의 V103 위치 오차 비교 Fig. 4 Position error of proposed and LC algorithm of V103

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그림 5 항법 모듈 및 실험 장비 Fig. 5 Navigation module and experimental setup

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그림 6 야외 실험 환경 Fig. 6 Outdoor experimental environments

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그림 7 수평 및 수직 위치 추정 결과 Fig. 7 Estimation results of horizontal and vertical position

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그림 8 위치 추정 오차 비교 Fig. 8 Estimation error of position

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그림 9 자세 추정 오차 비교 Fig. 9 Estimation error of attitude

표 1 EuRoC dataset의 위치, 자세 오차 결과 Table 1 Position and Attitude Error of EuRoC Dataset

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표 2 알고리즘 별 ATE 결과(median) Table 2 Absolute Translational RMSE (median)

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표 3 ADIS 16448 제원 Table 3 Specification of ADIS 16448

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