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


Biologically inspired mobile robot vision localization

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


Biologically inspired mobile robot vision localization

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


Biologically inspired mobile robot vision localization

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


Scene recognition1

Scene Recognition


Scene recognition2

Scene Recognition


Biologically inspired mobile robot vision localization

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


Biologically inspired mobile robot vision localization

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


Biological vision localization system

Biological Vision Localization System


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 gist feature extraction

Biological Vision Localization System – Gist Feature extraction


Biological vision localization system gist feature extraction1

Biological Vision Localization System – Gist 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 salient region recognition2

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


Biological vision localization system monte carlo localization3

Biological Vision Localization System – Monte Carlo Localization


Biologically inspired mobile robot vision localization

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


Experimental results rigid environment

Experimental Results – rigid environment

Lighting conditions test


Experimental results rigid environment1

Experimental Results – rigid environment

Lighting conditions test


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


Biologically inspired mobile robot vision localization

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


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