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

Stereo Vision Local Map Alignment for Robot Environment Mapping

Computer Vision Center

Dept. Ciències de la Computació


Ricardo Toledo Morales (CVC)

David Aldavert Miró (CVC)

Introduction Mapping

  • Data association problem:

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

  • Different approaches:

    • Cox

    • IDC

    • ICP

    • Correlation of histograms

    • Normal Distributions Transform

Introduction Mapping

  • 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.

Introduction Mapping

  • Using visual features:

Introduction Mapping

  • Using visual features in indoor environments:

    • Matching difficulties:

      • Depth discontinuities.

      • Lack of texture.

      • Repetitive texture.

Local stereo maps
Local stereo maps Mapping


Right image

Dense disparity map

Projection to the X-Z plane

Left image

  • Local obstacle maps from dense stereo:

Map alignment
Map alignment Mapping

  • 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 Mapping

  • 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 Mapping

  • Example:

Enhancing map alignment
Enhancing map alignment Mapping





  • 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 Mapping

  • Color obstacles:

    • For example:

Enhancing map alignment2
Enhancing map alignment Mapping

  • Example: avoiding ambiguities using color

Results Mapping

  • Alignment results:

Results Mapping

Increasing noise

Increasing noise

  • Ratio of correctly aligned maps:

Conclusions Mapping

  • 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.

Conclusions Mapping

Thank you for your attention!