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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (10): 327621-327621.doi: 10.7527/S1000-6893.2022.27621

• Electronics and Electrical Engineering and Control • Previous Articles    

Heterogeneous collaborative SLAM based on fisheye and RGBD cameras

Yutong ZHANG, Jianmei SONG, Yan DING(), Jinpeng LIU   

  1. School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China
  • Received:2022-06-14 Revised:2022-08-04 Accepted:2022-09-15 Online:2022-10-09 Published:2022-09-30
  • Contact: Yan DING E-mail:dingyan@bit.edu.cn
  • Supported by:
    National Natural Science Foundation of China(61873031)

Abstract:

SLAM technology has a the potential of wide applications in autonomous navigation in satellite navigation denied environments. To combine the advantages of monocular fisheye SLAM to obtain more texture information and RGBD-SLAM to directly obtain scale information, a heterogeneous collaborative SLAM system is designed based on a monocular fisheye camera and a RGBD camera. Firstly, a method for checking the 3D gray centroid direction consistency between feature points is designed to screen the candidate matching points between heterogeneous images. Then, a step-by-step optical flow and projection matching method between heterogeneous images is designed to achieve high-performance feature point matching and relative pose estimation between fisheye and RGBD camera. Finally, based on the ORB-SLAM2 framework, a heterogeneous collaborative SLAM system is proposed based on fisheye and RGBD camera. The experimental results show that compared with traditional feature point matching methods, the proposed feature point matching method shows higher performance in the task of image feature matching with heterogeneous cameras. Compared with the monocular fisheye SLAM and RGBD-SLAM system, the proposed heterogeneous collaborative SLAM system has better performance under the conditions of rapid camera movement, camera close to the scene, low frame rate, texture loss, pure rotation of the camera, outdoor large scenes, etc., and demonstrates improved robustness, anti-drift ability and trajectory accuracy.

Key words: heterogeneous collaborative SLAM, heterogeneous image feature matching, fisheye camera, RGBD camera, candidate matching points filtering

CLC Number: