Stereo vision local map alignment for robot environment mapping
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Stereo Vision Local Map Alignment for Robot Environment Mapping. Computer Vision Center Dept. Ciències de la Computació UAB. Ricardo Toledo Morales (CVC) David Aldavert Miró (CVC). Introduction. Data association problem:

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Stereo Vision Local Map Alignment for Robot Environment Mapping

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Stereo vision local map alignment for robot environment mapping

Stereo Vision Local Map Alignment for Robot Environment Mapping

Computer Vision Center

Dept. Ciències de la Computació

UAB

Ricardo Toledo Morales (CVC)

David Aldavert Miró (CVC)


Introduction

Introduction

  • Data association problem:

    • Put into correspondence robot sensor measurements obtained from different locations.

  • Different approaches:

    • Cox

    • IDC

    • ICP

    • Correlation of histograms

    • Normal Distributions Transform


Introduction1

Introduction

  • Using visual features:

    • Extract image features:

      • Harris Laplace/Affine, Hessian Laplace/Affine, MSER, SURF, IBR, EBR, ...

    • Characterize local features:

      • SIFT, GLOH, RIFT, PCA-SIFT, Shape Context, Steerable Filters, ...

    • Put features into correspondence:

      • Distance between descriptors, Hough, RANSAC, ...

    • Estimate robot motion from features correspondences.


Introduction2

Introduction

  • Using visual features:


Introduction3

Introduction

  • Using visual features in indoor environments:

    • Matching difficulties:

      • Depth discontinuities.

      • Lack of texture.

      • Repetitive texture.


Local stereo maps

Local stereo maps

Reconstruction

Right image

Dense disparity map

Projection to the X-Z plane

Left image

  • Local obstacle maps from dense stereo:


Map alignment

Map alignment

  • Build a PDF of the obstacles:

    • At each obstacles it is assigned a Gaussian distribution G(μ, σ) where:

      • μ is the location of the obstacle.

      • σ obtained from the covariance matrix of the stereo point reconstruction.


Map alignment1

Map alignment

  • Using a Gauss-Newton approach to search the robot motion parameters p = [α, tx, ty] that minimizes the following energy function:

  • As wrapping both PDF will be very computational expensive, only image points corresponding to obstacles of PD2 are taking into account in the matching process.


Map alignment2

Map alignment

  • Example:


Enhancing map alignment

Enhancing map alignment

Red

Green

Blue

Red

  • Color obstacles:

    • Add image properties to the obstacle maps to improve matching ratio and avoid ambiguities.

    • We used a simple color image segmentation method:

      • Change from RGB to HSV space.

      • Select points that have a significant color information:

        • Saturation * value = difference between the max and min RGB channel.

      • Assign pixel to a different channel depending on its hue.


Enhancing map alignment1

Enhancing map alignment

  • Color obstacles:

    • For example:


Enhancing map alignment2

Enhancing map alignment

  • Example: avoiding ambiguities using color


Results

Results

  • Alignment results:


Results1

Results

Increasing noise

Increasing noise

  • Ratio of correctly aligned maps:


Conclusions

Conclusions

  • The method can align two different local maps without performing explicit matching between obstacles.

  • The addition of color helps to increase the convergence ratio and solves some ambiguities.

  • The method is quite sensitive to the presence of outliers, specially when they are not uniformly distributed.

  • A more robust color segmentation could increase the overall method performance.

  • Even when two local maps are aligned using the environment structure, this is not enough to determine if both maps are really related. Additional knowledge that also takes into account environment appearance should help to avoid this ambiguities.


Conclusions1

Conclusions

Thank you for your attention!


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