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This paper introduces a new framework for incremental localization and mapping (3D-SLAM) that integrates submaps of surface features extracted from dense range images. Its local layer estimates unknown motion and feature associations between adjacent views. The global layer tries to ensure map consistency and to close loops, using multiple hypotheses offered by the local layer. The local algorithm crucially affects the map quality. An interpretation tree (IPT) is compared to Orthogonal Surface Assignment (OSA), a new algorithm tracing a building coordinate system inside man-made work spaces. During indoor experiments, performed with the rotating laser scanner RoSi, OSA proved much more reliable than IPT. Another experiment, running a simple on-line surface classification, indicates that the maps are useful for mission planning.

at - automatisierungstechnik

The Elastic View Graph Framework for Autonomous, Surface-based 3D-SLAM Part I: Concept and Local Layer (Schritthaltendes flächenbasiertes 3D-SLAM mit elastisch gekoppelten Teilkarten)