Kintinuous: Spatially Extended KinectFusion

TitleKintinuous: Spatially Extended KinectFusion
Publication TypeConference Paper
Year of Publication2012
AuthorsWhelan T, Kaess M, Fallon MF, Johnnsson H, Leonard JJ, McDonald JB
Conference NameRSS Workshop on {RGB-D}: Advanced Reasoning with Depth Cameras
Date Publishedjul
Conference LocationSydney, Australia
Abstract

In this paper we present an extension to the KinectFusion algorithm that permits dense mesh-based mapping of extended scale environments in real-time This is achieved through (i) altering the original algorithm such that the region of space being mapped by the KinectFusion algorithm can vary dynamically, (ii) extracting a dense point cloud from the regions that leave the KinectFusion volume due to this variation, and, (iii) incrementally adding the resulting points to a triangular mesh representation of the environment The system is implemented as a set of hierarchical multi-threaded components which are capable of operating in real-time The architecture facilitates the creation and integration of new modules with minimal impact on the performance on the dense volume tracking and surface reconstruction modules We provide experimental results demonstrating the system’s ability to map areas considerably beyond the scale of the original KinectFusion algorithm including a two story apartment and an extended sequence taken from a car at night In order to overcome failure of the iterative closest point (ICP) based odometry in areas of low geometric features we have evaluated the Fast Odometry from Vision (FOVIS) system as an alternative We provide a comparison between the two approaches where we show a trade off between the reduced drift of the visual odometry approach and the higher local mesh quality of the ICP-based approach Finally we present ongoing work on incorporating full simultaneous localisation and mapping (SLAM) pose-graph optimisation

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