Biologically inspired mobile robot vision localization
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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


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


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


Introduction their respective configuration relationships to form a signature of a location. Vision-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


Scene recognition1
Scene Recognition regions within the field of view.


Scene recognition2
Scene Recognition regions within the field of view.


Introduction regions within the field of view.Vision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work


Topological maps

  • A topological map is a graph annotation of an environment. regions within the field of view.

  • 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


Introduction regions within the field of view.Vision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work


Biological vision localization system
Biological Vision Localization System regions within the field of view.


Biological vision localization system1

The localization system is divided into three stages: regions within the field of view.

  • 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 features with stored environment information.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) involves:

  • 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

  • S used to deduce robot location.t 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: used to deduce robot location.

  • 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



Introduction LocalizationVision-based LocalizationScene RecognitionTopological MapsBiological Vision Localization System Experimental ResultsDiscussionRelated work


Experimental results rigid environment
Experimental Results – rigid environment Localization

Lighting conditions test


Experimental results rigid environment1
Experimental Results – rigid environment Localization

Lighting conditions test


Experimental results rigid environment2
Experimental Results – rigid environment Localization

Test System response on sparser scenes


Experimental results rigid environment3
Experimental Results – rigid environment Localization

Test System response on sparser scenes


Introduction LocalizationVision-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

Related Work


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