Micro Aerial Vehicle Navigation with a Single Omnidirectional

Micro Aerial Vehicle Navigationwith a Single Omnidirectional Camerag

(no Laser, no GPS)

Davide ScaramuzzaETH ZurichETH Zurich

Autonomous Systems Lab

Homepagehttp://asl.epfl.ch/~scaramuz/research/Davide_Scaramuzza_files/Research/OcamCalib_Tutorial.htm

Email: [email protected]

Motivation:sFly (EU FP7): Swarm of Micro Flying RobotssFly (EU FP7): Swarm of Micro Flying Robots

Multi-robot navigation in dense urban i tenvironments

Autonomous micro helicopters are about to play major roles in tasks like:

reconnaissance search and rescue environment monitoring securitysecurity surveillance inspection, etc.

Access to environments where no human or other vehicles gets access to

Reducing the risk for the environment and Reducing the risk for the environment and people

Tasks/Challenges

Multi-robot navigation in dense urban environments

Coordinated flight in small swarms in constrained and dense environments (e.g. urban)

Inter-distance estimation

Low power communication (between helicopters and with the ground station using GSM)

Inherently safe during operation

very low weight (< 500g)

appropriate propeller design

Autonomous navigation in GPS denied environments

Visual 3D mapping and navigation

Vision-based fully autonomous navigation and mapping

The “sFly” Consortium

ETH Autonomous Systems Lab

Integration and Visual SLAM

ETH C t i i G ETH Computer vision Group

Dense 3D mapping

INRIA Grenoble

Multi-robot SLAM

Technical University of Crete

Distributed control

CSEM

Swiss Center Electro Mechanics > inter helicopter communication Swiss Center Electro-Mechanics -> inter-helicopter communication

Ascending Technologies

Quadro-copters

Our GoalPerform automatically (without user intervention & without GPS):Perform automatically (without user intervention & without GPS):

Take-off

Set-point navigation and on-line 3D mapping (SLAM)

Landinga d g

… using a single camera as a main sensor.

Start Phase Flight Phase

Start mapping (on-line 6DoF SLAM) and navigate by following via points

Take off(without map)

Landing(with map)

Related Work on Vision-Aided Autonomous Navigation

Use of external cameras with known position to constantly track the vehicle (e.g. Viconmotion capture system)

The Vicon motion capture concept used at the ETH Zurich for tracking MAVs

Related Work on Vision-Aided Autonomous Navigation

Use of external cameras with known position to constantly track the vehicle (e.g. Viconmotion capture system)

On-board cameras:

Track known object (position and size known)

Optical flow for obstacle avoidance

Camera and laser pointers for collision avoidance (results of the muFly EU project)

The muFly helicopter realized at the ETH uses a miniature omnidirectional camera and 8 laser pointers to detect obstacles

< 5g < 80 g

The SystemThe PlatformThe Platform

Hummingbird quadrotor helicopter from Ascending Technologies (www.asctec.de) 50 cm diameter 50 cm diameter weight: 400 g plus 200 g of additional payload on-board IMU (roll, pitch, yaw) GPS position controller

Wide-angle Camera

GPS position controller

Fisheye lens with 190º FOV

3D Mapping using Monocular 6DOF SLAM

Test Environment

Hovering performance using Natural features

• Blösch, M., Weiss, S., Scaramuzza, D., and Siegwart, R., Vision Based MAV Navigation in Unknown and Unstructured Environments, IEEE International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, May, 2010.

• Eberli, D., Scaramuzza, D., Weiss, S., Siegwart, R., Vision based Position Control for MAVs using one single Artificial Landmark, International Conference on Unmanned Aerial Vehicles, Dubai June, 2010.

Hovering performance using Natural features

Time domain system response to an initial error of 1 m. The performance is limited by the relatively slow

measurement rate and the time delay of the system.y y



Hovering Performance above Different Outdoor Terrains under Windy Conditionsunder Windy Conditions



Autonomous Flight Strategy

Start Phase Flight PhaseInit Phase End Phase

Start mapping (on-line 3D SLAM) and navigate following via points

Take off(with blob)

Landing(with map)

Initializationof SLAM

frameworkframework(baseline & scale)



Demo of Autonomous Flying with a Single Camera Take off Hovering Set point following Landing Take-off, Hovering, Set-point following, Landing



Dataset Collection in the ETH Flying Machine Arena

The arena is 10m x 10m x 10m flying dedicated space for research involving The arena is 10m x 10m x 10m flying dedicated space for research involving flying vehicles

Vicon motion capture system tracks the 6DOF pose of the vehicle with millimeter positionmillimeter position

Recorded data from helicopter IMU Camera images (150 and 190 deg FOV)g ( g )

Features 6DoF visual motion estimation using several algorithms

Dataset (submitted to IROS’10) will be made available (upon acceptance!)

All the videos of this presentationAre available atAre available atwww.sfly.orgy g

(click on the YouTube link!)Davide Scaramuzza homepage:

http://asl.epfl.ch/~scaramuz/research/Davide_Scaramuzza_files/Research/OcamCalib_Tutorial.htm

Email: [email protected]

Documents

Micro Aerial Vehicle Navigation with a Single Omnidirectional