Overivew Occupancy Grids -Sonar Models -Bayesian Updating

Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Chapter 11: Localization and Map Making a. Occupancy Grids b. Evidential Methods c. Exploration

Chapter 11: Localization and Map Making
Objectives Describe the difference between iconic and feature-based localization Be able to update an occupancy grid using either Bayesian, DS, or HIMM Describe the two types of formal exploration strategies Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Where am I going? Mission planning What’s the best way there? Path planning Where have I been? Map making Where am I? Localization Navigation Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Carto- grapher Mission Planner deliberative How am I going to get there? Behaviors reactive Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Motivation Can make topological or metric maps, localize relative to landmark(s) or at any point More desirable: metric maps, localize at any point More readable by a human GPS isn’t the answer Localization error is on order of 1 meter Reception difficult indoors Want to know where features in environment are, not just robot (e.g., layout of walls, not just robot’s path) Sensor measurements have some uncertainty that must be factored in Formal methods called “evidential reasoning”, “theories of evidence” Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Basic Idea Integrate local map Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Global map Move D Local map Sense and create a local map Move a little Record change in position, orientation Fuse/tile together Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Observations about Process
Map is almost always a type of regular grid (because easier to visualize) The “Move D” and “Integrate local map” are the hard part. Integration requires accurate measurement of D (on order of inches and <=5 degrees) Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Black Is ground Truth, Purple is Measured Using shaft Encoders for D Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Iconic vs. Feature-Based
Issue is how to localize at each step to accurately measure D, then integrate local map Iconic: use raw (or near raw) sensor readings Match elements marked “empty” or “occupied” in a regular grid OCCUPANCY GRID Plug and chug, intense computations Feature-based: use features extracted from raw data Label and match corners, walls, whatever Less features, so less computations Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Occupancy Grids Type of regular grid L: eLement Came out of sonar tradition Each element is marked with belief that L is empty or occupied Usually a number on a scale [0,1] for probability and possibility theories [0-15] for HIMM Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Sonars and Occupancy Grids
Everything element L “under” the sonar beam gets marked with some value for empty, occupied Exact value depends on Sonar model Evidential method Generic sonar model 3 regions R: theoretical range, r: measured range b: half angle Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Evidential Methods for Occupancy Grids
Bayesian Popularized by Hans Moravec Dempster-Shafer HIMM Johan Borenstein Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Bayesian Compute the value for each L for each sonar using sonar model The value of L is a probablility Compute the value for each L where sonars overlap uses Bayes’ rule for updating Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Go to board and work example from book. This gives students a chance to write down notes, otherwise slides go too fast. Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Example: Value of L in Region II
Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Class Exercise: Value of L in Region I

Other Issues An element L may have multiple “hits” Robot moves and senses subset of same area, Sonars overlap: what to do? Use Bayes’ rule to update If write a program to use Bayes’ rule, what’s the initialization of the occupancy grid? P(Occupied)=P(Empty)=0.5 Is this a good assumption? Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Summary Localization and map making are intertwined Localization requires good maps Map making requires good localization Map making and localization techniques often use occupancy grids Type of regular grid Elements represent uncertainty of being empty, occupied Multiple ways of combining uncertainty when an element has multiple “hits” Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Dempster-Shafer Theory & HIMM
On board Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Iconic: uses raw sensor data directly Ex. Sonar and laser readings fused in an occupancy grid Compare current and past reading Feature-based: uses features extracted from sensor data Ex. “corners”, “walls” Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary ? Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Iconic Example: ARIEL Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Issues k must be small to be tractable, but k must be large if noisy sensors Doesn’t work with “just sonars” Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Iconic Example: ARIEL Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Results Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Exploration Can explore reactively (move to open area as per Donath), but we’d like to create maps Two major methods Frontier-based GVG Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Frontier Based Exploration
Robot senses environment Borders of low certainty form frontiers Rate the frontiers Centroid Utility of exploring (big? Close?) Move robot to the centroid and repeat (continuously localize and map as you go) Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

GVG Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Keeps moving, ignores areas hard to get too

Reaches deadend at 9, backtracks

Goes back and catches missing areas

Discussion of Exploration
Both methods work OK indoors, not so clear on utility outdoors GVG Susceptible to noise, hard to recover nodes Frontier Have to rate the frontiers so don’t trash Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Summary Map making requires Localization and acurate measurements Exploration Localization and map making often use Occupancy grids Evidential methods for updating Bayesian DS HIMM (quasi-evidential) Two kinds of localization: iconic, feature-based Two popular methods for exploration: frontier-based, GVG Overivew Occupancy Grids -Sonar Models -Bayesian Updating -Dempster-Shafer -HIMM Localization -ARIEL Exploration -Frontier-based -GVG Summary Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 11: Localization and Map Making

Overivew Occupancy Grids -Sonar Models -Bayesian Updating

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