A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera

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Expansion-based methods are among the fastest algorithms to detect obstacles for safe navigation of Micro-Aerial Vehicles (MAVs). These methods are based on estimating an enlarging rate which is mostly computed using point features. Using points alone may result in situations where obstacles are only partially identified. This paper presents a new technique that uses image regions, instead of points, for estimating the expansion rate. The proposed algorithm utilises a fisheye camera that can be installed in front of a drone to detect obstacles in all directions. The camera takes images on which obstacles are identified. At each point in time, we extract the regions on the latest fisheye image and check to determine whether or not its regions belong to an obstacle. This step is completed by matching region points with those within the previous image. If at least three points are matched, then the convex hulls of the matched points on both images are formed. The expansion ratio of the convex hull areas is then estimated. If this ratio is bigger than a certain threshold, the region on the latest image is determined as an obstacle; otherwise, it is disregarded. This process is repeated until all pixels of the image are labelled either as obstacle or non-obstacle. Experiments were carried out using 50 pairs of fisheye images that covered a variety of obstacles, including people, pillars, trees, walls, and so on. The findings showed between 74%, and 84% of pixels were labelled correctly. By comparing these results with those obtained by the method developed by Al-Kaff et al. (2017), it is clear that the proposed method produces more stable and accurate results.