This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera’s angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method’s proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 m. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems.

Computer vision is a new approach to navigation aiding that assists visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating their distance from the user, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. In this paper, we created a dataset of public objects in an uncontrolled environment for navigation aiding. The dataset contains three classes of objects which commonly exist on pavements in the city. It was verified that the dataset was of high quality for object detection and distance estimation, and was ultimately utilized as a navigation aid solution.