A Convolutional Neural Network Feature Detection Approach to Autonomous Quadrotor Indoor Navigation

Abstract

Object detection, extended to recognize and localize indoor structural features, is used to enable a quadrotor drone to autonomously navigate through indoor environments. The video stream from a monocular front-facing camera on-board a quadrotor drone is fed to an off-board system that runs a Convolutional Neural Network (CNN) object detection algorithm to identify specific features such as dead-ends, doors, and intersections in hallways. Using pixel-scale dimensions of the bounding boxes around the recognized objects, the distance to intersections, dead-ends and doorways can be estimated accurately using a Support Vector Regression (SVR) model to generate flight control commands for consistent real-time autonomous navigation at flight speeds approaching 2 m/s.