Life-Long Place Recognition by Shared Representative ... Place Recognition by Shared Representative...
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Transcript of Life-Long Place Recognition by Shared Representative ... Place Recognition by Shared Representative...
Life-Long Place Recognition by
Shared Representative Appearance Learning
Fei Han1, Xue Yang1, Yiming Deng2, Mark Rentschler3, Dejun Yang1, and Hao Zhang1
1. Colorado School of Mines 2. University of Colorado Denver 3. University of Colorado Boulder
Motivation Approach• We address the critical long-term place
recognition task with strong appearance variations due to changes of illumination, vegetation, weather, etc.
Hao Zhang, Ph.D.
Assistant Professor
Division of Computer Science
Colorado School of Mines
Phone: (303) 273-3581
Email: [email protected]
HCRobotics Lab: http://hcr.mines.edu
Contact
Results• Our method achieves the state-of-
the-art long-term place recognition performance, and outperforms baseline (feature concatenation) and previous (SeqSLAM, BRIEF-GIST, Color and LBP) methods .
Scene Representation• A set of heterogenous visual
features are utilized to capture image information and represent scenes.
Summary• We propose an innovative long-term place
recognition method based on Shared Representative Appearance Learning (SRAL) that is robust to strong appearance changes.
Optimization Formulation• Place recognition tasks are
formulated as a regularized sparse optimization
• ℓ2,1-norm regularization enforces sparsity of each row of 𝑾
• ℓM-norm regularization enforces sparsity between different feature modalities
Soundness• Algorithm 1 converges to the
global optimal solution
NotationX: scene feature matrix; Y: scene indicator matrix; W: weight matrix
Spring Summer
Autumn Winter
Morning
Afternoon
October
December
• Single feature modality or simple feature concatenation scheme cannot well represent the same place with appearance changes. Our method can learn and fuze multimodal features to build highly discriminative place representations that are robust to appearance changes.
Spring Summer Autumn Winter
Color
GIST
HOG
LBP
Raw
Nordland Dataset (Different Seasons)
Precision-recall curve Feature weight
Precision-recall curve Feature weight
St Lucia Dataset(Various Times of the Day)
CMU-VL Dataset (Different Months) Place Recognition with Fusion• Feature weight• Feature fusion for image matching
Precision-recall curve Feature weight
where 𝑠 is the matching score