This research focuses on enhancing stereo depth estimation techniques to operate effectively under challenging conditions on edge devices. The project aims to develop robust algorithms that can accurately estimate depth information in environments with varying lighting and weather conditions. By optimizing these algorithms for edge devices, the research ensures real-time processing and low-latency responses, which are crucial for portable navigation aids. The effectiveness of these improvements will be validated through a series of experiments, evaluating their performance in real-world scenarios.

This research focuses on testing and evaluating obstacle detection and avoidance solutions using Meta Aria smart glasses (and other available smart glasses technologies). The project will explore the integration of various detection algorithms and avoidance strategies with these wearable devices to assess their effectiveness in real-world environments.