Section: Application Domains

Simultaneous localization and mapping (SLAM)

Participants : James Crowley, Frédéric Devernay, Marion Decrouez.

Live processing of a video sequence taken from a single camera enables to model an a priori unknown 3D scene. Metrical SLAM (Simultaneous Localization and Mapping) algorithms track the camera pose while reconstructing a sparse map of the visual features of the 3D environment. Such approaches provide the geometrical foundation for many augmented reality applications in which informations and virtual objects are superimposed on live images captured by a camera. Improving such systems will enable in the future precise industrial applications such as guided-maintenance or guided-assembly in wide installations.

A problem with current methods is the assumption that the environment is static. Indoor environments such as supermarket ailes and factory floors may contain numerous objects that are likely to be moved, disrupting a localization and mapping system. We explore methods for automatic detection and modeling of such objects. We define the scene as a static structure that may contain moving objects and objects are defined as a set of visual features that share a common motion compared to the static structure [19] . Using several explorations of a camera in the same scene, we detect and model moved objects while reconstructing the environment. Experiments highlignt the performance of the method in a real case of localization in an unknown indoor environment.