Human behavior analysis from depth maps
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This paper describes an extension to the Monocular Simultaneous Localization and Mapping (MonoSLAM) method that relies on the images provided by a combined high resolution Time of Flight (HR-ToF) sensor. In its standard formulation MonoSLAM estimates the depth of each tracked feature as the camera moves. This depth estimation depends both on the quality of the feature tracking and the previous camera position estimates. Additionally, MonoSLAM requires a set of known features to initialize the scale of the map and the world coordinate system. We propose to use the combined high resolution ToF sensor to incorporate depth measures into the MonoSLAM framework while keeping the accuracy of the feature detection. In practice, we use a ToF (Time of Flight) and a high-resolution (HR) camera in a calibrated and synchronized set-up and modify the measurement model and observation updates of MonoSLAM. The proposed method does not require known features to initialize a map. Experiments show first, that the depth measurements in our method improve the results of camera localization when compared to the MonoSLAM approach using HR images alone; and second, that HR images are required for reliable tracking.