Accelerating trail navigation for unmanned aerial vehicle: A denoising deep-net with 3D-NLGL

Abstract

Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications.