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Distance Estimation to Support Assistive Drones for the Visually Impaired using Robust Calibration

Distance Estimation to Support Assistive Drones for the Visually Impaired using Robust Calibration

来源:Arxiv_logoArxiv
英文摘要

Autonomous navigation by drones using onboard sensors, combined with deep learning and computer vision algorithms, is impacting a number of domains. We examine the use of drones to autonomously assist Visually Impaired People (VIPs) in navigating outdoor environments while avoiding obstacles. Here, we present NOVA, a robust calibration technique using depth maps to estimate absolute distances to obstacles in a campus environment. NOVA uses a dynamic-update method that can adapt to adversarial scenarios. We compare NOVA with SOTA depth map approaches, and with geometric and regression-based baseline models, for distance estimation to VIPs and other obstacles in diverse and dynamic conditions. We also provide exhaustive evaluations to validate the robustness and generalizability of our methods. NOVA predicts distances to VIP with an error <30cm and to different obstacles like cars and bicycles with a maximum of 60cm error, which are better than the baselines. NOVA also clearly out-performs SOTA depth map methods, by upto 5.3-14.6x.

Suman Raj、Bhavani A Madhabhavi、Madhav Kumar、Prabhav Gupta、Yogesh Simmhan

航空自动化技术、自动化技术设备

Suman Raj,Bhavani A Madhabhavi,Madhav Kumar,Prabhav Gupta,Yogesh Simmhan.Distance Estimation to Support Assistive Drones for the Visually Impaired using Robust Calibration[EB/OL].(2025-03-31)[2025-05-23].https://arxiv.org/abs/2504.01988.点此复制

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