A Convolutional Neural Network Feature Detection Approach to Autonomous Quadrotor Indoor Navigation
Adriano Garcia, Sandeep S. Mittal, Edward Kiewra, Kanad Ghose
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.
BibTeX
@inproceedings{iros2019_aconvolutionalne,
title = {A Convolutional Neural Network Feature Detection Approach to Autonomous Quadrotor Indoor Navigation},
author = {Adriano Garcia and Sandeep S. Mittal and Edward Kiewra and Kanad Ghose},
booktitle = {IROS 2019},
year = {2019}
}