A Generalized Kalman Filter Augmented Deep-Learning based Approach for Autonomous Landing in MAVs
This addresses the critical need for robust autonomous landing in MAVs under challenging conditions like poor GPS resolution and environmental factors, though it appears incremental as it builds on existing detection schemes.
The paper tackles the problem of autonomous landing for Micro Aerial Vehicles (MAVs) by proposing a generalized end-to-end landing site detection system that avoids pre-assumptions about the landing site, achieving comparable accuracy and outperforming existing methods in landing time.
Autonomous landing systems for Micro Aerial Vehicles (MAV) have been proposed using various combinations of GPS-based, vision, and fiducial tag-based schemes. Landing is a critical activity that a MAV performs and poor resolution of GPS, degraded camera images, fiducial tags not meeting required specifications and environmental factors pose challenges. An ideal solution to MAV landing should account for these challenges and for operational challenges which could cause unplanned movements and landings. Most approaches do not attempt to solve this general problem but look at restricted sub-problems with at least one well-defined parameter. In this work, we propose a generalized end-to-end landing site detection system using a two-stage training mechanism, which makes no pre-assumption about the landing site. Experimental results show that we achieve comparable accuracy and outperform existing methods for the time required for landing.