A Generalized Kalman Filter Augmented Deep-Learning based Approach for Autonomous Landing in MAVs

TL;DR

This paper introduces a generalized deep learning approach with a Kalman filter for autonomous MAV landing, addressing various environmental and operational challenges without assuming specific landing site conditions.

Contribution

The work presents a novel end-to-end landing site detection system that does not rely on predefined assumptions, improving robustness and efficiency in MAV landings.

Findings

Achieves comparable accuracy to existing methods.

Reduces landing time compared to prior approaches.

Handles diverse environmental conditions effectively.

Abstract

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.