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2008
IEEE

3D-based monocular SLAM for mobile agents navigating in indoor environments

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3D-based monocular SLAM for mobile agents navigating in indoor environments
This paper presents a novel algorithm for 3D depth estimation using a particle filter (PFDE - Particle Filter Depth Estimation) in a monocular vSLAM (Visual Simultaneous Localization and Mapping) framework. We present our implementation on an omnidirectional mobile robot equipped with a single monochrome camera and discuss: experimental results obtained in our Assistive Kitchen project and its potential in the Cognitive Factory project. A 3D spatial feature map is built using an Extended Kalman Filter state-estimator for navigation use. A new measurement model consisting of a unique combination between a ROI (Region Of Interest) feature detector and a ZNSSD (Zero-mean Normalized Sum-of-Squared Differences) descriptor is presented. The algorithm runs in realtime and can build maps for table-size volumes.
Dejan Pangercic, Radu Bogdan Rusu, Michael Beetz
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where ETFA
Authors Dejan Pangercic, Radu Bogdan Rusu, Michael Beetz
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