Abstract
Vision-related state estimation usually extracts multiple feature points from images captured by the camera. In this paper, we propose a robust feature homogenization method for resolving the problem of feature clustering. The proposed method deduced the depth of feature points from optical flow magnitude, and the homogenization of feature points was acquired by adaptively enforcing the minimum distance between neighboring feature points. With the assistance of optical flow, the proposed method develops a preference for feature points with smaller depths in feature homogenization. Experimental results show that the proposed method improves the system’s global consistency and tracking stability by using optical flow information.
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Data Availability
The EuRoC micro aerial vehicle datasets, first published in Ref. [25], were acquired from Ref. [29]. The synthesized data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
25. M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, “The EuRoC micro aerial vehicle datasets,” Int. J. Robot. Res. 35, 1157–1163 (2016). [CrossRef]
29. Autonomous Systems Lab, The EuRoC micro aerial vehicle datasets, ASL/ETH Zurich, 2016, https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets (accessed on 23 October 2021).
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