Autonomous Intelligent Robotics Instructor: Shiqi Zhang - - PowerPoint PPT Presentation

Spring 2018 CIS 693, EEC 693, EEC 793:

Autonomous Intelligent Robotics

Instructor: Shiqi Zhang

http://eecs.csuohio.edu/~szhang/teaching/18spring/

Readings

• Grid-based vs. MCL

– Efficiency – Continuous/discretized

• … it does not specify how the final position is

chosen based on all these hypotheses. --- Mahmoud

– Average of N-Best particles? – Report the best-match particle?

Mapping

Why Mapping?

• Learning maps is one of the fundamental

problems in mobile robotics

• Maps allow robots to efficiently carry out

their tasks, allow localization …

• Successful robot systems rely on maps for

localization, path planning, activity planning etc.

The General Problem of Mapping

What does the environment look like?

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The General Problem of Mapping

• Formally, mapping involves, given the sensor

data, to calculate the most likely map

} , , , , , , {

2 2 1 1 n n z

u z u z u d  = ) | ( max arg

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Mapping as a Chicken and Egg Problem

• So far we learned how to estimate the pose of the

vehicle given the data and the map.

• Mapping, however, involves to simultaneously

estimate the pose of the vehicle and the map.

• The general problem is therefore denoted as the

simultaneous localization and mapping problem (SLAM).

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Types of SLAM-Problems

• Grid maps or scans

[Lu & Milios, 97; Gutmann, 98: Thrun 98; Burgard, 99; Konolige & Gutmann, 00; Thrun, 00; Arras, 99; Haehnel, 01;…]

• Landmark-based

[Leonard et al., 98; Castelanos et al., 99: Dissanayake et al., 2001; Montemerlo et al., 2002;…

10

Problems in Mapping

• Sensor interpretation

– How do we extract relevant information from raw

sensor data?

– How do we represent and integrate this information

• ver time?
• Robot locations have to be estimated

– How can we identify that we are at a previously

visited place?

– This problem is the so-called data association

problem.

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Incremental Updating

• f Occupancy Grids (Example)

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Resulting Map Obtained with Ultrasound Sensors

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Resulting Occupancy and Maximum Likelihood Map

The maximum likelihood map is obtained by clipping the occupancy grid map at a threshold

• f 0.5

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Occupancy Grids: From scans to maps

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Tech Museum, San Jose

CAD map

• ccupancy grid map

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Example Occupancy Map

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• Occupancy grid maps are a popular approach to represent

the environment of a mobile robot given known poses.

• In this approach each cell is considered independently

from all others.

• It stores the posterior probability that the corresponding

area in the environment is occupied.

• Occupancy grid maps can be learned efficiently using a

probabilistic approach.

• They store in each cell the probability that a beam is

reflected by this cell.

SLAM

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Given:

– The robot’s controls – Observations of nearby features

Estimate:

– Map of features – Path of the robot

The SLAM Problem

A robot is exploring an unknown, static environment.

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Structure of the Landmark-based SLAM-Problem

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Mapping with Raw Odometry

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SLAM Applications

Indoors Space Undersea Underground

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Representations

• Grid maps or scans

[Lu & Milios, 97; Gutmann, 98: Thrun 98; Burgard, 99; Konolige & Gutmann, 00; Thrun, 00; Arras, 99; Haehnel, 01;…]

• Landmark-based

[Leonard et al., 98; Castelanos et al., 99: Dissanayake et al., 2001; Montemerlo et al., 2002;…

24

Why is SLAM a hard problem?

SLAM: robot path and map are both unknown Robot path error correlates errors in the map

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Why is SLAM a hard problem?

• In the real world, the mapping between observations

and landmarks is unknown

• Picking wrong data associations can have

catastrophic consequences

• Pose error correlates data associations

Robot pose uncertainty

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SLAM:

Simultaneous Localization and Mapping

• Full SLAM:
• Online SLAM:

Integrations typically done one at a time

) , | , (

: 1 : 1 : 1 t t t

u z m x p

1 2 1 : 1 : 1 : 1 : 1 : 1

... ) , | , ( ) , | , (

−

∫∫ ∫

=

t t t t t t t

dx dx dx u z m x p u z m x p 

Estimates most recent pose and map! Estimates entire path and map!

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Graphical Model of Online SLAM:

1 2 1 : 1 : 1 : 1 : 1 : 1

... ) , | , ( ) , | , (

−

∫∫ ∫

=

t t t t t t t

dx dx dx u z m x p u z m x p 

28

Graphical Model of Full SLAM:

) , | , (

: 1 : 1 : 1 t t t

u z m x p

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Techniques for Generating Consistent Maps

• Scan matching
• EKF SLAM
• Fast-SLAM
• Probabilistic mapping with a single map and a

posterior about poses Mapping + Localization

• Graph-SLAM, SEIFs

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Scan Matching

Maximize the likelihood of the i-th pose and map relative to the (i-1)-th pose and map. Calculate the map according to “mapping with known poses” based on the poses and observations.

{ }

) ˆ , | ( ) ˆ , | ( max arg ˆ

1 1 ] 1 [ − − −

⋅ =

t t t t t t x t

x u x p m x z p x

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robot motion current measurement map constructed so far

] [

ˆ t m