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

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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

  • 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

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


Closing the loop

Closing the loop


Combining visual and spatial appearance for loop closure detection in slam

Image Feature Extraction Process

MSER detector

Saliency detector


Combining visual and spatial appearance for loop closure detection in slam

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


Combining visual and spatial appearance for loop closure detection in slam

Matching Performance

Query Image

Tentative Match

Similar posters found in the environment.

[Newman,Ho ICRA2005]

Tentative Match

Tentative Match


Combining visual and spatial appearance for loop closure detection in slam

Results from Image Retrieval System


Combining visual and spatial appearance for loop closure detection in slam

Limitations of Image Matching

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

  • Spatial information can be used to disambiguate visually confusing locations


Spatial descriptors

Spatial Descriptors

  • 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

  • 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

  • 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

  • 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

  • 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

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


Descriptor comparison 2

Descriptor Comparison 2

  • entropy disparity – Kullback-Leiber distance


Edge comparison

Edge Comparison

  • 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

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


Results from spatial retrieval system

Results from Spatial Retrieval System


More results

More Results


Combining visual and spatial appearance for loop closure detection in slam

MSER

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


Spatial similarity matrix

Spatial Similarity Matrix


Combined similarity matrix

Combined Similarity Matrix


Demonstration

Demonstration


Issues

Issues

  • 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

Thank you!


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