Autonomous Near Ground Quadrone Navigation with Uncalibrated Spherical Images Using Convolutional Neural Networks

Abstract

This paper focuses on the use of spherical cameras for autonomous quadrone navigation tasks. Previous works of literature for navigation mainly lie in two categories: scene-oriented simultaneous localization and mapping and robot-oriented heading fields lane detection and trajectory tracking. Those methods face the challenges of either high computation cost or heavy labelling and calibration requirements. In this paper, we propose to formulate the spherical image navigation as an image classification problem, which significantly simplifies the orientation estimation and path prediction procedure and accelerates the navigation process. More specifically, we train an end-to-end convolutional network on our spherical image dataset with novel orientation categories labels. This trained network can give precise predictions on potential path directions with single spherical images. Experimental results on our Spherical-Navi dataset demonstrate that the proposed approach outperforms the comparing methods in realistic applications.