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Biologically inspired Mobile Robot Vision Localization. Presenter Folami Alamudun Authors Christian Siagian Laurent Itti. Introduction Vision-based Localization Scene Recognition Topological Maps Biological Vision Localization System Experimental Results Discussion Related work. What?

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biologically inspired mobile robot vision localization

Biologically inspired Mobile Robot Vision Localization

Presenter

FolamiAlamudun

Authors

Christian Siagian

Laurent Itti

slide2

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

introduction

What?

    • Robot localization system using biologically inspired vision
  • Why?
    • Provide machines with a human-like perceptual system capable conducting intelligent localization in an unstructured environment.
  • How?
    • Biologically inspired scene summarization (gist) and landmark identification (saliency).
Introduction
slide4

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

vision based localization

Vision

    • Primary perceptual system for localization in most animals (including humans).
    • Effective in most environments where sonar, radar and GPS are unavailable or inoperable.
  • The human process of localization is performed using two processes:
    • Gist – A holistic statistical signature of the image, thereby yielding abstract scene classification and layout.
    • Saliency – A measure of interest at every image location and landmark-identification.
Vision-based Localization
vision based localization1

Vision-based localization systems use vision information to classify systems using:

  • Global features – A general summary of information over the entire image.
  • Local features – Computed over a limited area of the image
Vision-based Localization
vision based localization global features

Global feature methods generally consider an input image as a whole and extract a low-dimensional signature.

Advantage:

  • Provides a summary of the image statistics or semantics.
  • Robust because random local pixel noise averages out on global scale.

Disadvantages:

  • Sacrifices spatial information such as feature location and orientation.
  • Unable to define accurate pose estimation
  • Harder to deduce positional change even with significant robot movement.
Vision-based Localization – Global Features
vision based localization local features

Local feature methods limit their scope to image regions and their respective configuration relationships to form a signature of a location.

Advantage

  • local features encode scene characteristics that are more focused in scope.
  • Invariant in scale, in-plane rotation, viewpoint and lighting invariance.

Disadvantage

  • Very slow.
Vision-based Localization – Local Features
slide9

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

scene recognition

Human visual processing system uses visually interesting regions within the field of view.

  • Saliency-based selection of landmarks that are most reliable in a particular environment.
  • Focusing on specific regions for comparing different images makes for a less computationally expensive process
Scene Recognition
slide13

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

topological maps

A topological map is a graph annotation of an environment.

  • Topological Maps assign nodes to particular places and edges as paths if direct passage between pairs of places (end nodes) exist.
  • Humans manage spatial knowledge primarily by topological information.
  • This information is used to construct a hierarchical topological map that describes the environment.
Topological Maps
slide15

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

biological vision localization system1

The localization system is divided into three stages:

  • Feature extraction – Processes image to produce:
    • Gist features;
    • Salient regions.
  • Recognition - compares features with memorized environment visual information.
  • Localization – compute where the robot is situated.
Biological Vision Localization System
biological vision localization system feature extraction

Feature extraction involves processing of raw low-level filter outputs into gist and saliency modules.

  • Gist feature extraction
    • Computes average values from sub-regions of feature maps.
    • Dimensionality reduction using PCA/ICA
  • Salient region selection and segmentation
    • Uses feature maps to detect conspicuity regions in each channel.
Biological Vision Localization System – Feature extraction
biological vision localization system segment and salient region recognition

This stage attempts to match salient regions and gist features with stored environment information.

  • Segment estimator:
    • Three-layer neural network classifier trained using the back-propagation on gist features
  • Salient Region Recognition:
    • Recalls stored salient regions
    • Uses SIFT key points and salient feature vector to recognize regions.
Biological Vision Localization System – Segment and Salient Region Recognition
biological vision localization system2

Segment Estimation computes likelihood that a scene belongs to a segment:

  • Salient region localization provides a saliency map which highlights coordinates of peak values (salient points).
    • These points are used for identification purposes in subsequent viewing.
Biological Vision Localization System
biological vision localization system salient region recognition

Recollection of stored salient regions for localization involves:

  • SIFT keypoints
    • SIFT recognition system using parameters and thresholds.
  • Salient feature vector
    • A set of values taken from 5x5 window centered at the salient point location.
Biological Vision Localization System – Salient Region Recognition
biological vision localization system salient region recognition1

(continued)

  • Salient feature vectors form two salient regions (sreg1, sreg2) are compared using:
    • Similarity
    • Proximity
Biological Vision Localization System – Salient Region Recognition
biological vision localization system monte carlo localization

When a landmark is recognized its associated location is used to deduce robot location.

  • Accumulated temporal context is used to distinguish between identical landmarks.
  • Robot position is estimated by implementing Monte-Carlo Localization (MCL) which utilizes Sampling Importance Resampling (SIR).
Biological Vision Localization System – Monte Carlo Localization
biological vision localization system monte carlo localization1

St as a set of weighted particles:

    • St = {xt,i, wt,i}, (i = 1, . . . , N)
    • xt,i = {snumt,i , ltravt,i} (possible robot location)
      • snum – segment number
      • Ltrav – length traveled along segment edge
    • wt,i = weight likelihood.
    • at time t; and
    • N is the number of particles.
    • Bel(St) = location belief at time t.
    • ut = motion measurement
Biological Vision Localization System – Monte Carlo Localization
biological vision localization system monte carlo localization2

Belief estimation algorithm:

  • Apply motion model to St−1 to create St’ .
  • Apply segment observation model to St’ to create St’’.
  • IF (Mt > 0):
    • apply salient region observation model to St’’ to yield St ;
    • ELSE St = St’’.

Where:

    • St’ = is the belief state after application of motion model.
    • St’’ = is the state after the segment observation
Biological Vision Localization System – Monte Carlo Localization
slide30

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

experimental results rigid environment2
Experimental Results – rigid environment

Test System response on sparser scenes

experimental results rigid environment3
Experimental Results – rigid environment

Test System response on sparser scenes

slide35

IntroductionVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work

discussion

This paper introduced new ideas (the use of complementary gist and saliency features).

  • Saliency model lets the system automatically select persistent salient regions as localization cues.
  • Low computation cost gist features approximate the image layout and provide segment estimation.
  • System is able to compute coordinate level localization in multiple environments
  • Performance is comparable to GPS database guided systems.
Discussion
related work

Determining Patch Saliency Using Low-Level Context. European Conference on Computer Vision (ECCV), 2008. D. Parikh et. al.

Related Work