RGB-D Object Recognition for Semantic Navigation ENSTA ParisTech INRIA FLOWERS Team Louis-Charles Caron David Filliat What is semantic navigation ? Using high-level “semantic” information to navigate ENSTA ParisTech Thème Robotique & Vision 2 What is semantic navigation ? Using high-level “semantic” information to navigate ENSTA ParisTech Thème Robotique & Vision 3 What is a semantic map ? Non-semantic ENSTA ParisTech Semantic Thème Robotique & Vision 4 Défi Carotte – 2010 - 2012 Robotics competition – Organized by French Armament Procurement Agency (DGA) and Research Funding Agency (ANR) – 5 selected and funded teams – Artificial environment, 100 m2 Goals – Autonomous exploration – Metric mapping – Semantic information • Room segmentation • Ground/Wall types • Objects type and position ENSTA ParisTech Thème Robotique & Vision 5 Video ENSTA ParisTech Thème Robotique & Vision 6 Scene segmentation Original scene Floor removal Down-sampling Floor projection ENSTA ParisTech Clustering Thème Robotique & Vision Walls removal Interpretation 7 Feature computation Texture feature • SIFT, passed through a vocabulary Color features SIFT • Histogram of hue • Histogram of Normalized RGB channels Color Shape feature • Surflet-pair relation histogram Shape ENSTA ParisTech Thème Robotique & Vision 8 Object Database 52 objects 6 angles of view 100 snapshots / angle 1 to 4 meters distance ENSTA ParisTech Thème Robotique & Vision 9 Classification Classification • Concatenate feature vectors • Feed-forward neural network Color Shape SIFT Position awareness • Average scores of superposed objects in the map Feed-forward Neural Network P Class ENSTA ParisTech Thème Robotique & Vision 10 Resulting semantic map ENSTA ParisTech Thème Robotique & Vision 11 Recognition rates SIFT ENSTA ParisTech trgb Thème Robotique & Vision hue SPRH 12 Perspectives Classification • Classification depends on success of segmentation • Neural network is hard to interpret Database / Hard supervision Public databases ENSTA ParisTech Thème Robotique & Vision 13 Vote-based Local Recognition ENSTA ParisTech Thème Robotique & Vision 14 Unsupervised object discovery – Using laser range finder only ENSTA ParisTech Thème Robotique & Vision 15 Example trajectories Cluster « Sofa » Cluster «Stool » ENSTA ParisTech Thème Robotique & Vision 16 Recognition rates Halfway around object ENSTA ParisTech Thème Robotique & Vision 17 Thank you
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