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.

• Type: Conference
• Year: 2008
• Where: ETFA
• Authors: Dejan Pangercic, Radu Bogdan Rusu, Michael Beetz
• Keywords: Depth Estimation, Emerging Technology, ETFA 2008, Filter Depth Estimation, Omnidirectional Mobile Robot