Visual Navigation of UAVs in Indoor Corridor Environments using Deep Learning

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for various applications, including inspection, surveillance, delivery, and mapping. However, one of the significant challenges in enabling UAVs to perform these tasks is the ability to navigate in indoor environments. Visual navigation, which uses visual information from cameras and other sensors to localize and navigate the UAV, has received considerable attention in recent years. In this paper, we propose a new approach for visual navigation of UAVs in indoor corridor environments using a monocular camera. The approach relies on a novel convolutional neural network (CNN) called Res-Dense-Net, which is based on the ResNet and DenseNet networks. Res-Dense-Net analyzes the images captured by the UAV's camera and predicts the position and orientation of the UAV relative to the environment. To demonstrate the effectiveness of the proposed approach, experiments were conducted on the NitrUAVCorridorV1 dataset. The proposed approach achieves high accuracy in estimating the position and orientation of the UAV, even in challenging environments with limited visual cues and provides high real-time performance based on visual data from a monocular camera, which can significantly enhance the capabilities of UAVs for various applications.