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Unmanned aerial vehicles (UAV) technology has been an innovative advancement in the scientific environment over recent years. In this paper, we propose an approach that facilitates UAVs with a monocular camera combined with light detection and range (LiDAR) sensor to navigate autonomously for stairs climbing in completely unknown, GPS-denied indoor environments. The suggested approach utilizes a state-of-the-art CNN model for the task. We suggest a novel approach utilizing the video feed derived from the UAV front camera to determine the next maneuver in the deep neural network model. The process is viewed as a classification activity, where the deep neural network model classifies the image as a stair or no-stair and LiDAR sensor data are used for distance calculation. The training is performed from a dataset of images obtained from multiple stairs. We show the effectiveness of the proposed device in indoor stairs scenarios in real-time.