Combining visual and spatial appearance for loop closure detection in slam
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Combining Visual and Spatial Appearance for Loop Closure Detection in SLAM. Kin Leong Ho, Paul Newman Oxford University Robotics Research Group. Motivation. Loop Closing – the task of deciding whether a vehicle has returned to a previously visited area

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Combining visual and spatial appearance for loop closure detection in slam

Combining Visual and Spatial Appearance for Loop Closure Detection in SLAM

Kin Leong Ho, Paul Newman

Oxford University Robotics Research Group


Motivation
Motivation Detection in SLAM

  • Loop Closing – the task of deciding whether a vehicle has returned to a previously visited area

  • Popular approaches – nearest neighbour statistical gate, joint compatibility test


Image loop closure
Image Loop Closure Detection in SLAM

  • Closing loops with visually salient features to avoid dependence on global position estimate


Closing the loop
Closing the loop Detection in SLAM


Image Feature Extraction Process Detection in SLAM

MSER detector

Saliency detector


Demonstration of wide-baseline stability of visually salient features under perspective distortion and variation in illumination conditions


Matching Performance features under perspective distortion and variation in illumination conditions

Query Image

Tentative Match

Similar posters found in the environment.

[Newman,Ho ICRA2005]

Tentative Match

Tentative Match


Results from Image Retrieval System features under perspective distortion and variation in illumination conditions


Limitations of Image Matching features under perspective distortion and variation in illumination conditions

TentativeMatch

Query Image

Tentative Match

  • - Repetitive visual artifacts in urban environments such as posters, signs and wall pattern

  • False triggering of loop closure event based solely on image matching


Incorporating spatial information
Incorporating Spatial Information features under perspective distortion and variation in illumination conditions

  • Spatial information can be used to disambiguate visually confusing locations


Spatial descriptors
Spatial Descriptors features under perspective distortion and variation in illumination conditions

  • Reduced a laser scan patch into a set of descriptor

  • Describe curvature of shape

  • Describe complexity of shape

  • Describe spatial configuration of laser scan


Segmentation
Segmentation features under perspective distortion and variation in illumination conditions

  • Laser scan is divided into smaller but sizeable segments

  • Segments are formed due to break in boundary or occlusions

Original Laser Scan

Set of Descriptors


Cumulative angular function
Cumulative Angular Function features under perspective distortion and variation in illumination conditions

  • A plot of the cumulative change in turning angle versus the arc length of the segment

  • Invariant to rotation and translation

Turning

Angle

Arc length of Segment


Entropy of caf
Entropy of CAF features under perspective distortion and variation in illumination conditions

  • A measure of complexity of segment

  • Weight descriptors to prefer between complex versus simple shapes

CAF

Histogram of Turning Angle


Inter segment descriptors
Inter-Segment Descriptors features under perspective distortion and variation in illumination conditions

  • Extract critical points: Critical points are points along a segment where there are sharp changes in cumulative angular function

  • Distances and relative orientations between critical points form links between segments


Descriptor comparison 1
Descriptor Comparison 1 features under perspective distortion and variation in illumination conditions

  • Angular function disparity – minimum error between two cumulative angular functions


Descriptor comparison 2
Descriptor Comparison 2 features under perspective distortion and variation in illumination conditions

  • entropy disparity – Kullback-Leiber distance


Edge comparison
Edge Comparison features under perspective distortion and variation in illumination conditions

  • Matching of links

  • Links that are matched are coloured in black

  • Links that are not matched are coloured in blue


Spatial similarity score
Spatial Similarity Score features under perspective distortion and variation in illumination conditions

  • Shape similarity metric comprises of two parts: shape similarity and spatial similarity


Results from spatial retrieval system
Results from Spatial Retrieval System features under perspective distortion and variation in illumination conditions


More results
More Results features under perspective distortion and variation in illumination conditions


MSER features under perspective distortion and variation in illumination conditions

Detector

Query

Laser Scan

Query

Image

Selected

Regions

Saliency

Detector

Segmentation

SIFT

Descriptor

Laser

Descriptor

Laser Scan

Database

Image

Database

Combined

Similarity

Scores

Similarity

Measure

Similarity

Measure


Visual similarity matrix
Visual Similarity Matrix features under perspective distortion and variation in illumination conditions


Spatial similarity matrix
Spatial Similarity Matrix features under perspective distortion and variation in illumination conditions


Combined similarity matrix
Combined Similarity Matrix features under perspective distortion and variation in illumination conditions


Demonstration
Demonstration features under perspective distortion and variation in illumination conditions


Issues
Issues features under perspective distortion and variation in illumination conditions

  • Setting of threshold values

  • Principled way of combining similarity scores

  • At present limited to planar environments

Current Extensions

  • Removal of repetitive images by spectral decomposition

  • Successful Application to 3D laser mapping and SLAM


Questions
Questions features under perspective distortion and variation in illumination conditions

Thank you!


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