Learning to Fly by MySelf: A Self-Supervised CNN-Based Approach for Autonomous Navigation

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular facilitated by their extensive availability. Autonomous navigation methods can act as an enabler for the safe deployment of drones on a wide range of real-world civilian applications. In this work, we introduce a self-supervised CNN-based approach for indoor robot navigation. Our method addresses the problem of real-time obstacle avoidance, by employing a regression CNN that predicts the agent's distance-to-collision in view of the raw visual input of its on-board monocular camera. The proposed CNN is trained on our custom indoor-flight dataset which is collected and annotated with real-distance labels, in a self-supervised manner using external sensors mounted on an UAV. By simultaneously processing the current and previous input frame, the proposed CNN extracts spatio-temporal features that encapsulate both static appearance and motion information to estimate the robot's distance to its closest obstacle towards multiple directions. These predictions are used to modulate the yaw and linear velocity of the UAV, in order to navigate autonomously and avoid collisions. Experimental evaluation demonstrates that the proposed approach learns a navigation policy that achieves high accuracy on real-world indoor flights, outperforming previously proposed methods from the literature.

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

“…Among the DL techniques for collision avoidance there are end-to-end approaches that map directly the raw sensor data captured by the robotic system into a set of possible actions [48][49][50]. Such approaches are based on learning the behavior of an expert pilot at a given scenario in a real-world environment.…”

“…These DL approaches usually include a module for situational awareness that generates a set of feature maps related to the state of the robotic system and its surroundings, and then such computed feature maps feed up a second module for the decision-making process. Therefore, the combination of the mentioned two modules make up a complex network that takes raw sensor data as input and generates the motion control commands for the robotic system [48,[51][52][53][54][55].…”

“…This necessity involves human experts during the collection and annotation of the data. In order to minimize human effort, self-supervised algorithms automate the collection and annotation processes to generate large scale datasets [48,49].…”

“…The network makes use of two consecutive RGB images [48] Two stream CNN and returns the distance to obstacles in Indoors three directions.…”

Informed Region Selection for Efficient UAV-based Object Detectors: Altitude-aware Vehicle Detection with CyCAR Dataset

“…At the other end of the spectrum, UAV-based deep learning models can be in-the-loop of mission critical decisions including navigation and collision avoidance [5] [6]. In such cases, low-latency requirements are imposed for inference, making the cost of a wireless link between the drone and the base station prohibitive.…”