prof. dr. achim lilienthal mobile robotics and olfaction...
TRANSCRIPT
Mobile Robotics and Olfaction Lab, AASS, Örebro University
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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
# 6 © A. J. Lilienthal (Feb 14, 2012)
1. Profile + Applications
2. Requirements
3. Sensors
4. Selected Research Results
5. Future Work
Content
# 8 © A. J. Lilienthal (Feb 14, 2012)
1. Profile + Applications
2. Requirements
3. Sensors
4. Selected Research Results
5. Future Work
Content
# 9 © A. J. Lilienthal (Feb 14, 2012)
1. Profile + Applications
2. Requirements
3. Sensors
4. Selected Research Results
5. Future Work
Content
# 10 © A. J. Lilienthal (Feb 14, 2012)
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
MR&O Lab Profile – Two Major Research Directions
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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"
Autonomous Work Machines
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso) o environment with a dynamic "background" o requires 3D sensing
Autonomous Work Machines
1 meter “drop” to the railway tracks
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Autonomous Work Machines
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Autonomous Work Machines
picture from the actual mine!
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Autonomous Work Machines
# 23 © A. J. Lilienthal (Feb 14, 2012)
1.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Mobile Work Machines
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DustBot
Mobile Work Machines
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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
MR&O Lab Profile – Two Major Research Directions
# 27 © A. J. Lilienthal (Feb 14, 2012)
1.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Mobile Work Machines
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Requirements
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# 33 © A. J. Lilienthal (Feb 14, 2012)
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Requirements o perception o manipulation (for picking up and unloading material / goods) o planning and execution o safe navigation
Mobile Work Machines
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Requirements o perception o manipulation (for picking up and unloading material / goods) o planning and execution o safe navigation
Mobile Work Machines
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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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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, …
MR&O Lab Research Areas
coloured point cloud (an example of rich 3D data)
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Rich 3D Perception o rich 3D = spatial data augmented with additional information
» additional dimensions: e.g. colour, temperature, …
What is Rich 3D?
"thermal" point cloud (example of rich 3D data)
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Rich 3D Perception o rich 3D = spatial data augmented with additional information
» 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)
# 74 © A. J. Lilienthal (Feb 14, 2012)
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
# 75 © A. J. Lilienthal (Feb 14, 2012)
4.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
# 76 © A. J. Lilienthal (Feb 14, 2012)
4.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
# 77 © A. J. Lilienthal (Feb 14, 2012)
4.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
# 78 © A. J. Lilienthal (Feb 14, 2012)
4.
Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
# 79 © A. J. Lilienthal (Feb 14, 2012)
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Forklift Trucks (Danaher Motion, Linde MH, Stora Enso)
Wheel Loaders (VolvoCE, VolvoTech, NCC)
Mining Vehicles (Atlas Copco, Fotonic)
Hospital Transport Vehicles (RobCab)
Garbage Bin Collection and Cleaning (RoboTech)
Landfill Site Inspection (Atleverket)
Unloading Containers (Vollers, Qubica)
Rich 3D Perception Projects
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Rich 3D Perception o research question
» how can we create a consistent, useful world model from rich 3D data?
Selected Research Results
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Rich 3D Perception o research question
» 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?
Selected Research Results
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Rich 3D Perception o research question
» 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?
Selected Research Results
HDL-64E (≈75k$)
http://velodynelidar.com/lidar
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Rich 3D Perception o research question
» 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? • 3D-NDT (Normal Distribution Transform) [Magnusson et al., JFR 2007]
Selected Research Results
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Rich 3D Perception o research question
» 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? » scan registration def
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Selected Research Results
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Rich 3D Perception o research question
» 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? » 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]
Selected Research Results
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Rich 3D Perception o research question
» how can we create a consistent, useful world model from rich 3D data? o key contributions
» how to detect changes in rich 3D data? ("Find the Difference") • Difference Detection for Security Patrol Robots [Andreasson et al., IROS 2007]
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
# 106 © A. J. Lilienthal (Feb 14, 2012)
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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
⇐
# 112 © A. J. Lilienthal (Feb 14, 2012)
4.
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
# 113 © A. J. Lilienthal (Feb 14, 2012)
4.
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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Mobile Robot Olfaction o research question
» how do we enable intelligent gas-sensitive systems to perform in the real world?
Selected Research Results
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Mobile Robot Olfaction o research question
» how do we enable intelligent gas-sensitive systems to perform in the real world?
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]
Selected Research Results
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Mobile Robot Olfaction o research question
» how do we enable intelligent gas-sensitive systems to perform in the real world?
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]
Selected Research Results
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Mobile Robot Olfaction o research question
» how do we enable intelligent gas-sensitive systems to perform in the real world?
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]
Selected Research Results
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Mobile Robot Olfaction o research question
» how do we enable intelligent gas-sensitive systems to perform in the real world?
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]
Selected Research Results
search path: 0.5x random exploration
search path: 8x random exploration
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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
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Prof. Dr. Achim Lilienthal www.aass.oru.se/~lilien [email protected] Örebro & Reutlingen University