Depth Estimation Using Homographic Transformation

Localization is vital in various applications like location-based services, robotics, virtual reality, autonomous driving, and pedestrian navigation. Traditional methods using wireless signals and inertial measurement units have limitations. Image-based localization methods offer promising solutions, espe-cially indoors. However, indoor environments have challenges like dynamic changes and visual similarity between places. Estimating the depth of objects in computer vision is crucial for applications like object avoidance in robotics and augmented reality. In this paper, we present an approach that is based on homographic transformation to estimate the depth of indoor-localized objects.