Deep Learning on the Wing: A Survey of Resource-Efficient Object Detection and SLAM for Autonomous UAVs
TL;DR
A systematic classification of recent breakthroughs in real-time perception, specifically evaluating stereo image processing and Simultaneous Localization and Mapping through the lens of computational economy, and analyzes the efficacy of specialized optimization frameworks designed to maximize inference speed on embedded CPU/GPU architectures.
Abstract
: The evolution of Unmanned Aerial Vehicles (UAVs) into self-governing robotic entities is currently limited by the high computational overhead of modern neural networks. This review investigates the intersection of high-fidelity deep learning and the stringent resource limitations of edge-based aerial hardware. We present a systematic classification of recent breakthroughs in real-time perception, specifically evaluating stereo image processing and Simultaneous Localization and Mapping (SLAM) through the lens of computational economy. Drawing on extensive industrial experience in drone manufacturing and AI department leadership, this paper analyzes the efficacy of specialized optimization frameworks—such as multi-threaded frame tiling and hardware-concurrency mapping—designed to maximize inference speed on embedded CPU/GPU architectures. We further examine the role of spatio-temporal modeling and LSTM-based architectures in navigating unpredictable environments, while synthesizing the requirements for safety-critical, responsible AI deployment. By aligning theoretical algorithmic pruning with the practical realities of the product lifecycle, this survey provides a definitive technical roadmap for engineers and researchers aiming to achieve robust, on-board autonomy in the next generation of intelligent flight systems.