Towards Perception-based Collision Avoidance for UAVs when Guiding the Visually Impaired
This addresses the challenge of safe navigation for visually impaired individuals in urban settings, representing an incremental application of existing methods to a new domain.
The paper tackles the problem of using drones to assist visually impaired people in navigating outdoor urban environments by developing a perception-based path planning system for local obstacle avoidance, integrated with a global planner, and demonstrates feasibility in scenarios like footpaths, parked vehicles, and crowded streets.
Autonomous navigation by drones using onboard sensors combined with machine learning and computer vision algorithms is impacting a number of domains, including agriculture, logistics, and disaster management. In this paper, we examine the use of drones for assisting visually impaired people (VIPs) in navigating through outdoor urban environments. Specifically, we present a perception-based path planning system for local planning around the neighborhood of the VIP, integrated with a global planner based on GPS and maps for coarse planning. We represent the problem using a geometric formulation and propose a multi DNN based framework for obstacle avoidance of the UAV as well as the VIP. Our evaluations conducted on a drone human system in a university campus environment verifies the feasibility of our algorithms in three scenarios; when the VIP walks on a footpath, near parked vehicles, and in a crowded street.