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Ioannis Rekleitis
Ultrasonic Sensing
and Mapping
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Introduction to Mapping
• What the world looks like?
• Knowledge representation
– Robotics, AI, Vision
• Who is the end-user?
– Human or Machine
• Ease of Path Planning
• Uncertainty!
2CSCE 774: Robotic Systems
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Simultaneous Localization And Mapping
SLAM is the process of building a map of an environment while, at the same time, using that map to maintain the location of the robot.
• Problems for SLAM in large scale environments:
– Controlling growth of uncertainty and complexity
– Achieving autonomous exploration
3CSCE 774: Robotic Systems
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Consider this Environment:
4CSCE 774: Robotic Systems
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Three Basic Map Types
Topological:Collection of nodes and their interconnections
Grid-Based:Collection of discretized obstacle/free-space pixels
Feature-Based:Collection of landmark locations and correlated uncertainty
5CSCE 774: Robotic Systems
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Three Basic Map Types
Grid-Based Feature-Based Topological
Construction Occupancy grids Kalman Filter Navigation control
laws
Complexity Grid size and
resolution
Landmark covariance
(N3)
Minimal
complexity
Obstacles Discretized
obstacles
Only structured
obstacles
GVG defined by
the safest path
Localization Discrete localization Arbitrary localization Localize to nodes
Exploration Frontier-based
exploration
No inherent
exploration
Graph exploration
6CSCE 774: Robotic Systems
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Other Maps
Appearance
Based
Geometry
Based
Mesh
Based
Construction Images Lines, planes, etc Mesh
Path Planning N/A Geometry based Graph based
Localization Arbitrary
localization
Arbitrary
localization
Arbitrary
localization
7CSCE 774: Robotic Systems
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Mapping
Robot Map
World
8CSCE 774: Robotic Systems
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Mapping
Robot Map
World
•Indoor/Outdoor•2D/2.5D/3D•Static/Dynamic•Known/Unknown•Abstract (web)
9CSCE 774: Robotic Systems
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Mapping
Robot Map
World
•MobileIndoor/OutdoorWalking/Flying/Swimming
•Manipulator•Humanoid•Abstract
10CSCE 774: Robotic Systems
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Mapping
Robot Map
World
•Topological•Metric•Feature Based•1D,2D,2.5D,3D
11CSCE 774: Robotic Systems
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Mapping
Robot Map
World
•Topological•Metric•Feature Based•1D,2D,2.5D,3D
•MobileIndoor/OutdoorWalking/Flying/Swimming
•Manipulator•Humanoid•Abstract
•Indoor/Outdoor•2D/2.5D/3D•Static/Dynamic•Known/Unknown•Abstract (web)
12CSCE 774: Robotic Systems
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Sonar sensing
CSCE 774: Robotic Systems 13
Why is sonar sensing limited to
between ~12 in. and ~25 feet ?
“The sponge”
Polaroid sonar emitter/receivers
sonar timeline
0a “chirp” is emitted into the environment
75mstypically when reverberations from the initial chirp have stopped
.5sthe transducer goes into “receiving” mode and awaits a signal...
after a short time, the signal will be too weak to be detected
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Sonar effects
CSCE 774: Robotic Systems 14resolution: time / space
(d) Specular reflections cause walls to disappear
(e) Open corners produce a weak spherical wavefront
(f) Closed corners measure to the corner itself because of multiple reflections --> sonar ray tracing
(a) Sonar providing an accurate range measurement
(b-c) Lateral resolution is not very precise; the closest object in the beam’s cone provides the response
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Sonar modeling
CSCE 774: Robotic Systems 15
initial time response
spatial response
blanking time
accumulated
responses
cone width
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Sonar Modeling
CSCE 774: Robotic Systems 16
response model (Kuc)
sonar reading
obstacle
c = speed of sound
a = diameter of sonar element
t = time
z = orthogonal distance
a = angle of environment surface
• Models the response, hR, with:
a
• Then, add noise to the model to obtain a probability: p( S | o )
chance that the sonar reading is S,
given an obstacle at location o
z =
S
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Using sonar to create maps
CSCE 774: Robotic Systems 17
What should we conclude if this sonar reads 10 feet?
10 feet
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Using sonar to create maps
CSCE 774: Robotic Systems 18
What should we conclude if this sonar reads 10 feet?
10 feet
there is
something
somewhere
around here
there isn’t
something here
Local Mapunoccupied
occupied
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Using sonar to create maps
CSCE 774: Robotic Systems 19
What should we conclude if this sonar reads 10 feet?
10 feet
there is
something
somewhere
around here
there isn’t
something here
Local Mapunoccupied
occupied
or ...no information
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Using sonar to create maps
CSCE 774: Robotic Systems 20
What should we conclude if this sonar reads 10 feet...
10 feet
and how do we add the information that the next sonar reading (as the robot moves) reads 10 feet, too?
10 feet
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Combining sensor readings
CSCE 774: Robotic Systems 21
• The key to making accurate maps is combining lots of data.
• But combining these numbers means we have to know what they are !
What should our map contain ?
• small cells
• each represents a bit of the robot’s environment
• larger values => obstacle
• smaller values => free
what is in each cell of this sonar model / map ?
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What is it a map of?
CSCE 774: Robotic Systems 22
Several answers to this question have been tried:It’s a map of occupied cells.
oxy oxycell (x,y) is occupied
cell (x,y) is unoccupied
Each cell is either occupied or unoccupied -- this was the approach taken by the Stanford Cart.
pre ‘83
What information should this map contain, given that it is created with sonar ?
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What is it a map of ?
CSCE 774: Robotic Systems 23
Several answers to this question have been tried:
It’s a map of occupied cells.
It’s a map of probabilities: p( o | S1..i )
p( o | S1..i )
The certainty that a cell is occupied, given the sensor readings S1, S2, …, Si
The certainty that a cell is unoccupied, given the sensor readings S1, S2, …, Si
oxy oxycell (x,y) is occupied
cell (x,y) is unoccupied
• maintaining related values separately?
• initialize all certainty values to zero
• contradictory information will lead to both values near 1
• combining them takes some work...
‘83 - ‘88
pre ‘83
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Sonars from P/S
CSCE 774: Robotic Systems 24
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Sonar Locations Pioneer 3DX Robot
CSCE 774: Robotic Systems 25
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Sonar Data Calculation
CSCE 774: Robotic Systems 26
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Combining probabilities
CSCE 774: Robotic Systems 27
How to combine two sets of probabilities into a single map ?
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What is it a map of ?
CSCE 774: Robotic Systems 28
Several answers to this question have been tried:
It’s a map of occupied cells.
It’s a map of probabilities:
It’s a map of odds.
The certainty that a cell is occupied, given the sensor readings S1, S2, …, Si
The certainty that a cell is unoccupied, given the sensor readings S1, S2, …, Si
The odds of an event are expressed relative to the complement of that event.
The odds that a cell is occupied, given the sensor readings S1, S2, …, Si
oxy oxycell (x,y) is occupied
cell (x,y) is unoccupied
‘83 - ‘88
pre ‘83
probabilities
)|( 1 iSop
)|( 1 iSop
)|(
)|()|(
1
11
i
ii
Sop
SopSoodds
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An example map
CSCE 774: Robotic Systems 29
units: feet
Evidence grid of a tree-lined outdoor path
lighter areas: lower odds of obstacles being present
darker areas: higher odds of obstacles being present
how to combine them?
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Conditional probability
CSCE 774: Robotic Systems 30
Some intuition...
p( o | S ) =
The probability of event o, given event S .
The probability that a certain cell o is occupied, given that the robot sees the sensor reading S .
p( S | o ) = The probability of event S, given event o .
The probability that the robot sees the sensor reading S , given that a certain cell o is occupied.
•What is really meant by conditional probability ?
•How are these two probabilities related?
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Bayes Rule
CSCE 774: Robotic Systems 31
- Conditional probabilities
)()|()( SpSopSop
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Bayes Rule
CSCE 774: Robotic Systems 32
- Conditional probabilities
- Bayes rule relates conditional probabilities
Bayes rule
)()|()( SpSopSop
)(
)()|()|(
Sp
opoSpSop
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Bayes Rule
CSCE 774: Robotic Systems 33
- Conditional probabilities
- Bayes rule relates conditional probabilities
Bayes rule
)()|()( SpSopSop
)(
)()|()|(
Sp
opoSpSop
- So, what does this say about odds( o | S2 S1 ) ?
Can we update easily ?
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Combining evidence
CSCE 774: Robotic Systems 34
So, how do we combine evidence to create a map?
What we want --
odds( o | S2 S1) the new value of a cell in the map
after the sonar reading S2
What we know --
odds( o | S1) the old value of a cell in the map
(before sonar reading S2)
the probabilities that a certain obstacle
causes the sonar reading Si
p( Si | o ) & p( Si | o )
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Combining evidence
CSCE 774: Robotic Systems 35
)|(
)|()|(
12
1212
SSop
SSopSSoodds
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Combining evidence
CSCE 774: Robotic Systems 36
)()|(
)()|(
)|(
)|()|(
12
12
12
1212
opoSSp
opoSSp
SSop
SSopSSoodds
definition of odds
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Combining evidence
CSCE 774: Robotic Systems 37
)()|()|(
)()|()|(
)()|(
)()|(
)|(
)|()|(
12
12
12
12
12
1212
opoSpoSp
opoSpoSp
opoSSp
opoSSp
SSop
SSopSSoodds
definition of odds
Bayes’ rule (+)
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Combining evidence
CSCE 774: Robotic Systems 38
)|()|(
)|()|(
)()|()|(
)()|()|(
)()|(
)()|(
)|(
)|()|(
12
12
12
12
12
12
12
1212
SopoSp
SopoSp
opoSpoSp
opoSpoSp
opoSSp
opoSSp
SSop
SSopSSoodds
definition of odds
Bayes’ rule (+)
conditional independence of
S1 and S2
Bayes’ rule (+)
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Combining evidence
CSCE 774: Robotic Systems 39
)|()|(
)|()|(
)()|()|(
)()|()|(
)()|(
)()|(
)|(
)|()|(
12
12
12
12
12
12
12
1212
SopoSp
SopoSp
opoSpoSp
opoSpoSp
opoSSp
opoSSp
SSop
SSopSSoodds
definition of odds
Bayes’ rule (+)
conditional independence of
S1 and S2
Bayes’ rule (+)
Update step = multiplying the previous odds by a precomputed weight.
previous oddsprecomputed values
the sensor model
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Evidence grids
CSCE 774: Robotic Systems 40
hallway with some open doors lab space
known map and estimated evidence grid
CMU -- Hans Moravec
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Learning the Sensor Model
CSCE 774: Robotic Systems 41
The sonar model depends dramatically on the environment-- we’d like to learn an appropriate sensor model
rather than hire Roman Kucto develop another one...
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Learning the Sensor Model
CSCE 774: Robotic Systems 42
The sonar model depends dramatically on the environment-- we’d like to learn an appropriate sensor model
rather than hire Roman Kucto develop another one...
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Learning the Sensor Model
CSCE 774: Robotic Systems 43
part of the learned model
the mapping results of a model that had an even better match score (against the ideal map)
the idealized model
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Sensor fusion
CSCE 774: Robotic Systems 44
Incorporating data from other sensors -- e.g., IR rangefinders and stereo vision...
(1) create another sensor model
(2) update along with the sonar
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Centerline
• Only consider region of significant response
• Approximate response with an arc of uniform probability
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Choosing the center pointof the arc limits error
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Centerline
• Advantages
– Minimal computation required per sonar reading
– Low latency
• Disadvantages
– Inaccurate
– Open areas may appear occluded
only centerline points displayed
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Fusing Multiple Readings
• Regions of Constant Depth (RCDs)– Leonard et al. 1995
• Arc Tangents– McKerrow 1993
• Arc Transversal Median (ATM)– Choset and Nagatani 1999
• Line Fitting– MacKenzie and Dudek 1994
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Arc Carving Sonar Model
• Represents a sonar return as a cone with an arc base
– The arc approximates the sonar response
– The interior of the cone represents a region of likely freespace
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Occupancy Grid Sonar Model
• The arc carving model may be viewed as a binary approximation of the model used by Moravec and Elfes– An Arc with nonzero
probability of occupancy
– A cone with nonzero probability of freespace
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Arc Carving
• Each new sonar reading is checked against a history of previous readings
• If an arc is overlapped by the interior of a newer cone, the arc is “carved” to reflect this new information
• The updated arc is smaller, and therefore has a smaller bound on the error
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Arc Carving
• Multiple passes of Arc Carving may completely remove an arc
– Spurious sonar readings are removed
– Response to dynamic environments is increased
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Example – Ordinary Centerline
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Example – Arc Carving
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Arc Carving Video
• Latency issues are avoided
• The readings are more accurate than centerline
• Multiple reading approaches can be run off of the carved data
only carved points displayed
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Experimental Results:Centerline Map
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Experimental Results:Arc Carving Map
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