vision for mobile robot navigation jannes eindhoven 2-3-2010
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TRANSCRIPT
Vision for mobile robot navigation
Jannes Eindhoven
2-3-2010
Contents
Introduction [2] Indoor navigation
Map based approaches [5] Map building [1] Mapless navigation [2]
Outdoor navigation In structured environments [3] In unstructured environments [1]
Summary [1]
Introduction
Guilherme DeSouza
Avinash Kak
Introduction[2]
Summary of the developments of the last 2 decades.
February 2002, thus not including latest developments
Not all-comprising Gives examples of achievements
Indoor navigation – map based
Acquire sensory information Detect landmarks Establish matches between observation
and expectation Calculate position
Map based – absolute localization
Initial position is unknown Multi belief system Known landmarks from a map Calculate the position, incorporating the
uncertainty in the landmark locations Metric map
Map based – incremental localization
Start position is known Uncertainty in position is projected in
camera image Only use features in their expected image
parts The position gets updated
Map based – incremental localization [2]
Map based – Landmark tracking
Artificial landmarks Natural landmarks Geometric and
even topological representations
Example: NEURO-NAV
Map building
Slow process Additional problem to localization Generating occupancy grid or topological
map with metric representation at nodes
Mapless navigation
No explicit map Storing instructions as direct association
with perception
Mapless navigation – optical flow
Corridor following Viewing sideways, measuring surface
speed and proximity of both walls Direction determined by PID controller Problems with walls with little visible
features
Mapless navigation - Appearance-based matching
Memorizing the environment Associate commands or controls with
these images Like a train with a movie as “track” Can be simplified by matching only vertical
edges
Outdoor navigation
Changing lightning is challenging Main application is car automation
Outdoor navigation – Structured environments
Navlab's ALVINN Neural network with picture or Hough
transformed picture as input Lighting and shadows are a problem
Outdoor navigation – Structured environments [2]
Virtual camera images, extracted from the original camera image
Red and blue contrasts
Speed is required for automotive applications
Hue / intensity images
Outdoor navigation - Unstructured
Measuring local environment metrical Example: Pathfinder rover and lander
Conclusions
In controlled environments a lot can be achieved with current knowledge
In free or unpredictable environments, there is still a long way to go