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Roland Siegwart Margarita Chli Martin Rufli Davide Scaramuzza

ETH Master Course: 151-0854-00L

Autonomous Mobile Robots

Introduction

Key Questions and Concepts in Autonomous Mobile Robotics

The three key questions in Mobile Robotics
Where am I ? Where am I going ? How do I get there ?

To answer these questions the robot has to

have a model of the environment (given or autonomously built) perceive and analyze the environment find its position/situation within the environment plan and execute the movement
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1 - Introduction

Generic Control Scheme for Mobile Robot Systems

knowledge, data base mission commands

Localization Map Building environment model local map Information Extraction raw data

position global map

Cognition Path Planning path Path Execution actuator commands

Sensing

Acting

Real World Environment

1 - Introduction
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Motion Control

Perception

Control Architectures / Strategies Control Loop

dynamically changing environment no compact model available many sources of uncertainties
Localization position global map

Two Approaches
Classical AI
complete modeling model based horizontal decomposition

Cognition

environment model local map

path Motion Control

New AI, AL
sparse or no modeling behavior based vertical decomposition bottom up
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Perception

Real World Environment

1 - Introduction

Mixed Approach Depicted into the Generic Control Scheme

Localization

position / global map position / global map local map local map
perception to action

Cognition

obstacle avoidance

environment model local map

position feedback

Perception
1 - Introduction

Real World Environment

Motion Control

path
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Environment Representation and Modeling

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Environment Representation and Modeling: How we do it

Odometry
not applicable

Modified Environments
expensive, inflexible
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Feature-based Navigation
121 95

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still a challenge for artificial systems

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Elevator door

Corridor crossing

How to find a treasure Landing at night

Entrance Eiffel Tower

1 - Introduction
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Human Navigation: Topology with rough metric information

~ 400 m

~ 1 km ~ 200 m

~ 50 m ~ 10 m

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Environment Representation: The Map Categories

Topological Maps (Recognizable Locations)

Metric Topological Maps

Fully Metric Maps (continuous or

discrete)
y
200 m

50 km 2 km

100 km

{W}
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x
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Courtesy K. Arras

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From Perception to Understanding

Places / Situations
A specific room, a meeting situation,

Fusing & Compressing Information

Servicing / Reasoning

Objects
Doors, Humans, Coke bottle, car ,

Functional / Contextual Relationships of Objects

imposed learned spatial / temporal/semantic

Interaction

Models / Semantics

Features
Lines, Contours, Colors, Phonemes,

imposed learned

Navigation

Models

Raw Data
Vision, Laser, Sound, Smell,

imposed learned

1 - Introduction

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Understanding Probabilistic Reasoning (e.g. Bayesian)

Reasoning in the presence of uncertainties and incomplete information Combining preliminary information and models with learning from experimental data

Picture Courtesy of Bessiere, INRIA Grenoble, France

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Improving belief state by moving SEE and ACT

The robot is placed somewhere in the environment

Location unknown
x

The robot queries its sensors (SEE)

finds itself next to a pillar

The robot moves one meter forward (ACT)

Motion estimated by wheel encoders Accumulation of uncertainty

The robot queries its sensors again (SEE)

finds itself again next to a pillar

Updates its belief by combining this information with its previous belief
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Metric Navigation: Probabilistic Position Estimation

Kalman Filter Localization
Continuous, recursive and very compact

SEE
Matching

ACT

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State Prediction through Odometry

Incrementally (dead reckoning)
Odometry (wheel encoder) and/or initial sensors (gyro) Drift -> increasing uncertainty

ACT

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Measurement Prediction and Observation

Measurement Prediction
Using state prediction and e.g. metric map

Observation
e.g. feature (line segments) extracted from laser scan

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Matching and Estimation

Matching:
Find correspondence of features
Observation

Prediction

Estimation (new position):

Weighted mean (prediction & observation) e.g. Kalman filter, Markov

Observation

Prediction (Odometry)

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Different Approaches to Belief Representation

a. Continuous map with single hypothesis
(e.g. Kalman Filter)

b. Continuous map with multiple hypothesis

(e.g. Multiple Kalman Filters)

c. Discretized map with probability distribution

(e.g. Markov Localization)

d. Discretized topological map with probability distribution

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Discretizes Map Grid-Based Metric Approach

Grid Map of the Smithsonians National Museum of American History in Washington DC. Markov Localization Grid: ~ 400 x 320 = 128000 points

1 - Introduction

Courtesy S. Thrun, W. Burgard

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Grid-Based SLAM
(Simultaneous Localization and Mapping) Partial Filter to reduce computational complexity

Courtesy of Sebastian Thrun

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Probabilistic 3D SLAM

raw data
raw 3D scan of the same scene

find a plane for every cell using RANSAC

fuse similar neighboring planes together

segmented planar segments

1 - Introduction
final segmentation
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one plane per grid cell

photo of the scene

decompose space into grid cells fill cells with data

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Probabilistic 3D SLAM

close-up of reconstructed bookshelves close-up of a reconstructed hallway

Incremental map-building using a probabilistic estimation process. magenta: odometry only; blue: SLAM
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Data Compression: 99%

J. Weingarten
along its way (140 m), the robot takes 90 3D scans; the total number of planar segments is 244 (44696 data points / 299 polygons). This corresponds to a compression ratio of more than 99% w.r.t. raw data (5212800 points).

the robot lacks sensors to estimate 3D trajectories ICP or laser-corrected odometry allows to simulate a 6D odometry. This makes reconstruction of nonflat environments possible

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Maps for Planning: Exploration and Graph Construction

1. Exploration
explore on stack already examined

2. Graph Construction

provides correct topology must recognize already visited location backtracking for unexplored openings
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Where to put the nodes?

Topological: at distinctive locations Metric-based: where features disappear or get visible
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Some examples of todays state-of-the-art mobile robots

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PR2: Personal Robot 2

Robot for research and experimentation Development platform:
Cameras, Laser scanners, Accelerometer, Tactile sensors 16 CPU cores Sophisticated joints design for safety Variety of networking tools for communicating data
Inside the PR2

ROS: Robot Operating System free, open source, software development platform integrating libraries and tools Cost: $400 000

Courtesy of Willow Garage

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PR2: applications

Fold towels

Courtesy of Clean-up with cart

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Humanoid Robot: ASIMO

Hondas ASIMO - Advanced Step in Innovative MObility Designed to help people in their everyday lives One of the most advanced humanoid robots
Compact, lightweight Sophisticated walk technology Human-friendly design

Video: Honda 2012

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Google: Autonomous Driving in traffic

October 2010 Self-driving car in real traffic Toyota Prius + a variety of sensors:
Lidar (Laser) Video camera Radars GPS receiver Wheel encoders etc.

Autonomous Driving:
sense the surroundings mimic the decisions of a human driver

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Google: Autonomous Driving in traffic

Plan route like a GPS navigator but use extra data to decide on driving actions Boost safety & efficiency 7 cars,140000 miles with minimal human intervention Autonomous cars are still years from mass production

Video: ABC News

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EUROPA
European Robotic Pedestrian Assistant
The Team: ETH Zurich, University of Freiburg, Univ. of Oxford, KU Leuven, RWTH Aachen, BlueBotics

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NIFTi Urban Search and Rescuing

Project goals Robotic help for Urban Search and Rescue UGV and UAV combined for scene exploration Yearly evaluation of system by firemen Environment modeling Online 3D mapping from laser sensor Based on enhanced ICP released open-source Topological segmentation for human-robot interaction
The Team: ETH Zurich, DFKI Saarbrucken, TNO Soesterberg, Fraunhofer St. Augustin, BlueBotics, Czech Technical University, La Sapienza University of Rome, Fire Department of Dortmund, Ministry of the Interior Italy
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Omnicam Rotating Laser

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sFly: Swarm of micro Flying Robots

Recently finished EU project Coordinated flight in small swarms over previously unknown areas Autonomous micro helicopters for: Access to dangerous environments:
inspection, exploration, search & rescue, monitoring & surveillance

Vision-only fully autonomous navigation

GPS-denied environments
www.sfly.ethz.ch/

The Team:
ETH Zurich, (Autonomous Systems Lab and Computer Vision and Geometry Group); Ascending Technologies; Technical University of Crete; INRIA Grenoble; CSEM

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Feature Traking BRISK

BRISK:
Binary Robust Invariant Scalable Keypoints Performance comparable to SURF/SIFT speed up to 10 times faster than SURF (CPU)

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sFly Swarm of micro Flying robots with feature based visual navigation

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Content of the Course

1. 2. 3. 4. 5. 6. Introduction Locomotion Mobile Robot Kinematics Perception Mobile Robot Localization and Mapping Planning and Navigation

Specific aspect and potential applications are presented along the course

1 - Introduction

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Slides and additional material

Slides and Exercises
http://www.asl.ethz.ch/education/master/mobile_robotics Currently you can find there the last years slides and exercises. This years material will gradually become available throughout the term

Relevant reading material:

Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza Intelligent Robotics and Autonomous Agents series The MIT Press, Massachusetts Institute of Technology Cambridge, Massachusetts 02142 ISBN 0-262-19502-X

http://www.mobilerobots.org

1 - Introduction

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