Enhanced V-SLAM combining SVO and ORB-SLAM2, with reduced computational complexity, to improve autonomous indoor mini-drone navigation under varying conditions

Mini-drones have a wide range of applications, including weather monitoring, parcel delivery, search and rescue, and entertainment. As their functionality, safety, and performance heavily relies on ubiquitously reliable positioning and navigation, their applications are relatively limited to outdoor environments where Global Positioning System (GPS) and/or similar are available. Indoor localization is improving, e.g, using Visual Simultaneous Localization and Mapping (V-SLAM). However, for the applications of mini-drone navigation with a higher safety requirements, further improvements are still needed. This paper proposes a novel approach to improve the localization performance for mini-drone indoor navigation. The proposed approach enhances V-SLAM techniques by combining Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-direct monocular Visual Odometry (SVO) algorithms along with an Adaptive Complementary Filter (ACF). The results show that the proposed approach improvement in position estimation in the different conditions (low light, low texture and dynamic environments) in comparison with other commonly used indoor positioning approaches.