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.