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Mapping and Localization for Robots
The Occupancy Grid Approach
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Agenda
Introduction Mapping
Occupancy grids Sonar Sensor Model Dynamically Expanding Occupancy Grids
Localization Iconic Feature-based Monte Carlo
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An intelligent robot is a mechanical creature which can function autonomously.
Intelligent – the robot does not do things in a mindless, repetitive way.
Function autonomously – the robot can operate in a self-contained manner, under reasonable conditions, without interference by a human operator.
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Robots in museums
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Personal Robots
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Robots in space
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The problem of Navigation
Where am I going? What’s the best way there? Where have I been? Where am I? How am I going to get there?
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Mapping
Topological Mapping Features and Landmarks Milestones with connections Hard to scale
Metric Mapping Geometric representations Occupancy Grids Larger maps much more computationally intensive
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Map Making
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Demo of Mapping
The Littlejohn Project http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/mercator/www
/
littlejohn/
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Occupancy Grids
A tool to construct an internal model of static environments based on sensor data.
The environment to be mapped is divided into regions.
Each grid cell is an element and represents an area of the environment.
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Representation of Occupancy Grids
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Sonar Sensor Model
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Methods of Sonar Reading
Probabilistic Methods: Bayesian Dempster-Shafer HIMM (Histogrammic In Motion Mapping)
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Why Probabilistic Mapping?
Noise in commands and sensors Commands are not executed exactly
(eg. Slippage leads to odometry errors) Sonars have several error issues
(eg. cross-talk, foreshortening, specular reflection)
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Occupancy Grids
Pros Simple Accurate
Cons Require fixed-size environment:
difficult to update if size of mapped area changes.
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Dynamically Expanding Occupancy Grids
Variable-sized maps Ability to increase size of map, if new areas
are added to the environment Start mapping at center of nine-block grid As robot explores, new cells are added Global map is stored outside the RAM in a
file or a database
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Representation of DEOGs
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Adding Cells to a DEOG
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Dynamically Expanding Occupancy Grids
Best (the only?) solution for mapping changing environments.
Saves RAM Other useful information can be stored in the
map More complicated to program than regular
occupancy grids
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Localization
Where am I?
Methods: Iconic Feature-based Monte-Carlo
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Iconic Localization
Use raw sensor data Uses occupancy grids Current map is compared with original map.
If original map has errors, localization is very inaccurate.
Localization errors accumulate over time.
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The Concept
“pose”: (x, y, θ)location, orientation
Compare small local occupancy grid with stored global occupancy grid.
Best fit pose is correct pose.
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Feature-based Localization
Compares currently extracted features with features marked in a map.
Requires presence of easily extractable features in the environment.
If features are not easily distinguishable, may mistake one for the other.
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Monte Carlo Localization
Probabilistic 1. Start with a uniform distribution of possible poses (x, y,
) 2. Compute the probability of each pose given current
sensor data and a map 3. Normalize probabilities
Throw out low probability points Performance
Excellent in mapped environments Need non-symmetric geometries
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References:
Introduction to AI RoboticsDr. Robin Murphy
Dynamically Expanding Occupancy GridsBharani K. Ellore
Multi-agent mapping using dynamic allocation utilizing a storage systemLaura Barnes, Richard Garcia, Todd Quasny, Dr. Larry Pyeatt
Robotic Mapping: A surveySebastian Thrun
Littlejohn Projecthttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/mercator/www/littlejohn/
CYE www.prorobotics.com The Honda Asimo http://asimo.honda.com Mars Rover http://marsrovers.jpl.nasa.gov/home/