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Robust Autonomous Flight in Constrained and Visually Degraded Environments

Zheng Fang, Shichao Yang, Sezal Jain, Geetesh Dubey, Silvio Mano Maeta, Stephan Roth, Sebastian Scherer, Yu Zhang and Stephen T. Nuske
Conference Paper, Carnegie Mellon University, Field and Service Robotics, June, 2015

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This paper addresses the problem of autonomous navigation of a micro aerial vehicle (MAV) inside a constrained shipboard environment for inspection and damage assessment, which might be perilous or inaccessible for humans especially in emergency scenarios. The environment is GPS-denied and visually degraded, containing narrow passageways, doorways and small objects protruding from the wall. This makes existing 2D LIDAR, vision or mechanical bumper-based autonomous navigation solutions fail. To realize autonomous navigation in such challenging environments, we propose a fast and robust state estimation algorithm that fuses estimates from a direct depth odometry method and a Monte Carlo localization algorithm with other sensor information in an EKF framework. Then, an online motion planning algorithm that combines trajectory optimization with re- ceding horizon control framework is proposed for fast obstacle avoidance. All the computations are done in real-time onboard our customized MAV platform. We validate the system by running experiments in different environmental conditions. The results of over 10 runs show that our vehicle robustly navigates 20m long corridors only 1m wide and goes through a very narrow doorway (66cm width, only 4cm clearance on each side) completely autonomously even when it is completely dark or full of light smoke.

BibTeX Reference
title = {Robust Autonomous Flight in Constrained and Visually Degraded Environments},
author = {Zheng Fang and Shichao Yang and Sezal Jain and Geetesh Dubey and Silvio Mano Maeta and Stephan Roth and Sebastian Scherer and Yu Zhang and Stephen T. Nuske},
booktitle = {Field and Service Robotics},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2015},
address = {Pittsburgh, PA},