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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 - PowerPoint PPT Presentation
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Combining Visual and Spatial Appearance for Loop Closure
Detection in SLAM
Kin Leong Ho, Paul NewmanOxford University Robotics Research Group
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
• Closing loops with visually salient features to avoid dependence on global position estimate
Closing the loop
MSER detector
Saliency detector
Image Feature Extraction Process
Demonstration of wide-baseline stability of visually salient features under perspective distortion and variation in illumination conditions
Matching Performance
Similar posters found in the environment.
[Newman,Ho ICRA2005]
Query Image Tentative Match
Tentative MatchTentative Match
Results from Image Retrieval System
Limitations of Image Matching
- Repetitive visual artifacts in urban environments such as posters, signs and wall pattern
- False triggering of loop closure event based solely on image matching
Query Image Tentative MatchTentative Match
Incorporating Spatial Information
-Spatial information can be used to disambiguate visually confusing locations
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
• 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
• A plot of the cumulative change in turning angle versus the arc length of the segment
• Invariant to rotation and translation
Arc length of Segment
TurningAngle
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
• 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
• Angular function disparity – minimum error between two cumulative angular functions
Descriptor Comparison 2
• entropy disparity – Kullback-Leiber distance
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
•Shape similarity metric comprises of two parts: shape similarity and spatial similarity
Results from Spatial Retrieval System
More Results
Query Image
MSER Detector
Saliency Detector
SelectedRegions
SIFTDescriptor
ImageDatabase
Similarity Measure
LaserDescriptor
Laser ScanDatabase
Similarity Measure
Combined Similarity
Scores
Segmentation
QueryLaser Scan
Visual Similarity Matrix
Spatial Similarity Matrix
Combined Similarity Matrix
Demonstration
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
Thank you!