LAUSR

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to, among other features, their versatility, reduced operation cost, and reduced size. These applications increasingly require that features related to autonomous navigation be employed, such as mapping. However, the reduced capacity of resources such as battery life and hardware (memory and processing capacity) can hinder the development of these applications in UAVs. Thus, the collaborative use of multi‐UAVs for mapping can be used as an alternative to solve this problem, creating a cooperative navigation system. This system requires that individual local maps will be merged into a global map in a distributed way. In this context, this work proposes a system for merging three‐dimensional occupancy grid maps for use in UAVs that compose a cooperative navigation system. The proposed solution consists of a keypoint detector, a keypoint descriptor, and filters for keypoints and correspondences. The keypoint properties, such as orientation, are acquired from potential field gradients. Image processing techniques are applied to remove the map noise and better calculate the transformation parameters. The proposed system is validated by a set of simulation experiments performed in six different environments (indoor and outdoor). First, each component of the proposed system is individually evaluated. Then, the complete pairwise map merging system is evaluated. Finally, the appropriate functioning of the proposed system is validated through a visual evaluation.