prof. dr. achim lilienthal mobile robotics and olfaction...

Mobile Robotics and Olfaction Lab, AASS, Örebro University

# 1

Prof. Dr. Achim Lilienthal www.aass.oru.se/~lilien [email protected] Örebro & Reutlingen University

# 5 © A. J. Lilienthal (Feb 14, 2012)

1. Profile + Applications

2. Requirements

3. Sensors

4. Selected Research Results

5. Future Work

Content



2. Requirements

3. Sensors


5. Future Work

Content


Profile + Applications

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General Focus ... o perception systems for mobile robots

(fundamentals for autonomous and safe operation)

Objective ... o advance theoretical and practical foundations that allow mobile

robots to operate in an unconstrained, dynamic environment

Approaches are Characterized by ... o fusion of different sensor modalities o timely integration into industrial demonstrators

MR&O Lab Profile


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D1 – Mobile Robotics o for autonomous and safe long-term operation in the real world o technology transfer through collaborative projects with industrial

partners in the area of logistics robots

MR&O Lab Profile – Two Major Research Directions


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D1 – Mobile Robotics o for autonomous and safe long-term operation in the real world o technology transfer through collaborative projects with industrial

partners in the area of logistics robots o examples: autonomous forklifts and autonomous wheel loaders



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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)

Autonomous Work Machines

speed x 2


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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso) o environment with a dynamic "background"



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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso) o environment with a dynamic "background" o requires 3D sensing


1 meter “drop” to the railway tracks


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Wheel Loaders (VolvoCE, VolvoTech, NCC)



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Mining Vehicles (Atlas Copco, Fotonic)


picture from the actual mine!


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Hospital Transport Vehicles (RobCab)



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Garbage Bin Collection and Cleaning (RoboTech)

Mobile Work Machines


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DustBot



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D2 – Artificial and Mobile Robot Olfaction o Artificial Olfaction = gas sensing with artificial sensor systems o we study particularly open sampling systems def o develop "electronic nose" towards a "mobile nose" o examples: gas sensor networks (air pollution monitoring), mobile

robots for surveillance of landfill sites, gas leak localization



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Landfill Site Inspection (Atleverket)



Requirements

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Requirements o perception o manipulation (for picking up and unloading material / goods) o planning and execution o safe navigation



Selected Research Results

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Major Research Areas we are internationally recognized for work in ... o rich 3D perception o robot vision o mobile robot olfaction

MR&O Lab Research Areas


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Rich 3D Perception o rich 3D = spatial data augmented with additional information

» additional dimensions: e.g. colour, …


coloured point cloud (an example of rich 3D data)


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» additional dimensions: e.g. colour, temperature, …

What is Rich 3D?

"thermal" point cloud (example of rich 3D data)


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» additional dimensions: e.g. colour, temperature, remission, semantic label, confidence estimate, …

What is Rich 3D?

xi = (xi, yi, zi, r(1)i, ..., r(n)

i)


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Unloading Containers (Vollers, Qubica)

Rich 3D Perception Projects


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Landfill Site Inspection




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Rich 3D Perception o research question

» how can we create a consistent, useful world model from rich 3D data?



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» how can we create a consistent, useful world model from rich 3D data? o key contributions

» how to create compact, consistent world models from rich 3D data?



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» how to create compact, consistent world models from rich 3D data?


HDL-64E (≈75k$)

http://velodynelidar.com/lidar


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» how to create compact, consistent world models from rich 3D data? • 3D-NDT (Normal Distribution Transform) [Magnusson et al., JFR 2007]



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» how to create compact, consistent world models from rich 3D data? » scan registration def

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» how to create compact, consistent world models from rich 3D data? » scan registration def

• Iterative 3D-NDT Scan Registration [Magnusson et al., JFR 2007 / Magnusson et al., ICRA 2009]

• NDT-2-NDT Registration [Stoyanov et al., ICRA 2012? / Stoyanov et al., IJRR 2012?]

• Registration using Depth-Interpolated Local Image Features [Andreasson/Lilienthal, RAS 2010]



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» how to detect changes in rich 3D data? ("Find the Difference") • Difference Detection for Security Patrol Robots [Andreasson et al., IROS 2007]


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Mobile Robot Olfaction o research question

» how do we enable intelligent gas-sensitive systems to perform in the real world?


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o key contributions » how do we combine gas sensor measurements into a spatial model?

• spatial model of mean concentration with Kernel DM [Lilienthal et al., RAS 2004 / IROS 2003]


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o key contributions » how do we combine gas sensor measurements into a spatial model?

• spatial model of mean concentration with Kernel DM [Lilienthal et al., RAS 2004 / IROS 2003]

• spatial of mean and variance concentration … • … with GP Mixture Model [Stachniss et al., AURO 2009 / RSS 2008]

• ... with Kernel DM+V [Lilienthal et al., IROS 2009 / ISOEN 2009]


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o key contributions » how to include wind information when creating the gas distribution model?

• (3D-)Kernel DM+V/W [Reggente/Lilienthal, IEEE Sensors 2009 / IEEE Sensors 2010]



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o key contributions » gas source localization

• spatial averaging is important (higher speeds are preferable!) [Lilienthal et al., ICRA 2001] • gradient-following works very poorly [Lilienthal et al., AR 2004 / ICAR 2003]


search path: 0.5x random exploration

search path: 8x random exploration


Future Work

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Gas Source Localisation – Mobile Robot Olfaction o get rid of simplifying assumptions

» steady constant airflow » presence of a single gas source » gas source that emits a known chemical compound » gas source that emits at a constant rate » exploration area of limited size » robot's starting position is located downwind with respect to the gas source

Outlook


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Inspection Robots o Firebot

» mostly 2D problem » thermal vision? » rich 2D?

Outlook


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Inspection Robots o 3D models o sensor fusion

» model-based sensor fusion o sensor planning o combination of

probabilistic and analytical models

Outlook


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Transport Robots – Open Research Problems o dynamic environment o 3D, rich 3D (flat floor assumption does not hold)

» large amount of data, compact yet versatile representation required

o online solutions » incremental updates » constrained resources

o high speeds » scanning-while-moving » constrained resources

o robust solutions » outdoor conditions » graceful degradation wrt errors

o efficiency (e.g. compared to human operator)

Outlook


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Toyota SLAM Benchmark 2006

Outlook – Dynamic Environment

Mobile Robotics and Olfaction Lab, AASS, Örebro University

# 151

Prof. Dr. Achim Lilienthal www.aass.oru.se/~lilien [email protected] Örebro & Reutlingen University

prof. dr. achim lilienthal mobile robotics and olfaction...

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