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Mobile Robot Navigation in Outdoor Environments: A Topological Approach

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Compass (orientation), Gyroscope (roll, pitch and yaw) Sonars/Laser (free area, corners ... Even when a sensor retrieves poor amount of information, as a gyroscope ... – PowerPoint PPT presentation

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Title: Mobile Robot Navigation in Outdoor Environments: A Topological Approach


1
Mobile Robot Navigation in Outdoor
EnvironmentsA Topological Approach
Alberto Manuel Martinho Vale
Dissertação para a obtenção do Grau de Doutor
em Engenharia Electrotécnica e de Computadores
Instituto Superior Técnico Instituto de
Sistemas e Robótica
2
Motivation - Novelties
  • Topological maps, a type of representation with a
    high level of abstraction
  • The three main problems (localization, navigation
    and mapping) are addressed simultaneously
  • A linkage between this high level of abstraction
    and motion commands
  • Dynamic Expectation-Maximization algorithm for
    map building
  • The feature extraction and selection
    methodologies
  • Probabilistic approach for localization and
    navigation with a revisited Forward-Backward
    algorithm

How did we come to PA3 ? Referential? Counting
the steps?
3
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

4
The Mobile Robot Navigation Problem
Environment Model
5
Environment Representation
6
The adopted representation Topological Maps
  • Provides crucial information to travel long
    distances
  • Prepared to cope with the diversity of scenarios
    (large spectrum of information)
  • Robust to scenario changes
  • Useful for map sharing (mainly for heterogeneous
    robots)
  • Improve hierarchical representation, i.e.,
    different levels of topological representation
    with different resolutions

7
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

8
Localization
Given a sequence of observations, where is the
robot in the map?
9
Log of Prob. Localization
The topological map is defined by 6 states, each
one characterized by 5 different type of features
10
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

11
Navigation
12
Low Level Navigation
  • Reactive navigation (Behavior approach
    Attractive and Repulsive behaviors) to transform
    a sequence of states in a sequence of motion
    commands

Motion Commands
Target direction
Attractive behavior
(the target direction is ?ij from state si to sj)
13
Simulation Results Navigation
14
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

15
Mapping Expectation-Maximization Algorithm
i 1 , 2 , ... , N
N states and the respective Gaussians aij ,
state transition probabilities ?ij , direction
between states
16
Mapping Dynamic EM
Dynamic EM to determine N and based on the
entropy of state si
(wij probability of oi ? state sj)
The state transition probabilities aij and the
directions between states ?ij, are given
integrating the localization results in the
current map
17
Topological Map Features
  • Robustness to small displacements
  • Invariant to lighting conditions
  • Invariant to occlusion
  • Fast computation
  • Capacity to compress the images as much as
    possible while retaining pertinent information

18
Feature Selection
Select the best features to reduce the ambiguity
to the environment representation
and
19
Features Correlation (results)
Exp.1 Pav. Central Exp.2
Garden Exp.3 Parking
Compression from observations to features
Correlation between features
20
Summary
21
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

22
Experimental Results (indoors)
Features acquired from range sensors average
free-area (measured by laser and by sonars) and
variance of free-area (by laser)
23
Experimental Results (outdoors)
  • Features acquired from images and orientation
    sensors (colour and 2D histograms, edges and
    orientation)
  • Results with PCA/ICA provide more ambiguities

Via points
24
Experimental Results (outdoors)
Mapping
Localization
25
Experimental Results (outdoors)
Mapping
Localization
26
Structure
  • Mobile Robot Navigation
  • Problem Definition
  • Environment Representation
  • The adopted representation Topological Maps
  • Localization
  • Markov Models (to support robot navigation)
  • Changed version of Forward-Backward Algorithm
  • Navigation
  • Path Planning
  • Low Level Driving Methodology
  • Mapping
  • Dynamic Expectation-Maximization Algorithm
  • Features Extraction
  • Features Selection
  • Initialization
  • Experimental Results
  • Indoor
  • Outdoor
  • Conclusions and Future Directions

27
Conclusions
  • It is possible to develop a topological approach
    for the navigation at a high level of abstraction
    and implemented in simple outdoor environments
  • Even when a sensor retrieves poor amount of
    information, as a gyroscope
  • Localization, navigation and mapping are
    addressed at the same level of abstraction
  • Scenario may be represented by a topological map
    with a low resolution and therefore, with each
    state incorporating another topological map or
    even a metric map
  • Feature extraction and selection are important
    issues of the topological approach, since the
    representation is highly dependent to the type of
    features

28
Further Research
  • The precision and resolution of a topological map
  • Linkage between states (not necessarily by
    orientations)
  • Combining the localization, navigation and
    mapping together in a real situation of rescue
    scenarios
  • Cooperative navigation develop the topological
    approach using a team of robots
  • Different approaches to evaluate the features
    correlation to improve the mapping algorithm
  • Features data processing (different types of
    sensors)

29
Mobile Robot Navigation in Outdoor
EnvironmentsA Topological Approach
Alberto Manuel Martinho Vale
Dissertação para a obtenção do Grau de Doutor
em Engenharia Electrotécnica e de Computadores
Instituto Superior Técnico Instituto de
Sistemas e Robótica
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