GOPE: Geometry-aware optimal viewpoint path estimation using a monocular camera

The goal of the optimal viewpoint path estimation is to generate a path to the optimal viewpoint location where the robot can best see the Point of Interest (POI). There are several learning-based methods to find an optimal viewpoint, but these methods are limited to a specific object POI and it is necessary to newly learn in a situation where a new POI is added, and not robust to the environment changes. In this paper, we propose an algorithm that generates a path to the optimal viewpoint by using the geometrical features of the environment in the situation where the target POI is in the field of view. This method makes it easy to add new POIs and is robust to environmental changes because it uses semantic and geometric information. We assume that the robot can make a simple estimation of the geometric characteristics of the surrounding environment by using pretrained networks or by using sensor values. We collected the Kwanjeong street dataset for testing our algorithm. In this dataset, the distance accuracy of our method to reach the optimal viewpoint of the POI achieved 81.8% and 70.9% for template matching accuracy.