Improving Visual SLAM by Combining SVO and ORB-SLAM2 with a Complementary Filter to Enhance Indoor Mini-Drone Localization under Varying Conditions

Abstract

Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization in order to preserve safety and performance. In indoor scenarios, technologies such as Visual Simultaneous Localization and Mapping (V-SLAM) are used instead. However, more advancements are still required for mini-drone navigation applications, especially in the case of stricter safety requirements. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-Direct Monocular Visual Odometry (SVO) via an Adaptive Complementary Filter (ACF), the proposed strategy achieves better position estimates under various conditions (low light in low-surface-texture environments and high flying speed), showing an average percentage error of 18.1% and 25.9% smaller than that of ORB-SLAM and SVO against the ground-truth.

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones

“…High-accuracy localization for drones in indoor environments has become a critical task [ 13 , 14 , 15 , 16 ]. There are various use cases where it is crucial for drones to precisely determine their location indoors in order to perform autonomous navigation and execute tasks effectively [ 17 , 18 , 19 , 20 ]. For example, in large warehouses where drones are deployed for organizing goods, it is essential for them to accurately know their location and identify specific inventory locations.…”

Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

“…However, this comes at the cost of an increased computational demand, especially when compared to the efficiency of feature-based methods like ORB-SLAM. There are also combined techniques; for example, semi-direct SLAM is combined with feature-based SLAM [22].…”