Performance Evaluation of Vision-Based Algorithms for MAVs
This work addresses safety improvements for MAV operations in unstructured indoor settings, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper compares vision-based real-time algorithms for localization and mapping in Micro Aerial Vehicles (MAVs) to enhance safety in GPS-denied indoor environments, highlighting their strengths and weaknesses without reporting specific performance numbers.
An important focus of current research in the field of Micro Aerial Vehicles (MAVs) is to increase the safety of their operation in general unstructured environments. Especially indoors, where GPS cannot be used for localization, reliable algorithms for localization and mapping of the environment are necessary in order to keep an MAV airborne safely. In this paper, we compare vision-based real-time capable methods for localization and mapping and point out their strengths and weaknesses. Additionally, we describe algorithms for state estimation, control and navigation, which use the localization and mapping results of our vision-based algorithms as input.