A Probabilistic Framework for Object Descriptions in Indoor Route Instructions

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments

“…Outdoor environments contain clear decision points, present in street networks, and have less complex spatial layouts when compared to indoor environments [28].…”

“…Mast et al [28] propose a probabilistic framework for generating route instructions for indoor scenarios, that aims to provide context by relating instructions to environmental features. However, the authors do not consider saliency for identifying important indoor objects which are present in conversational dialogue between humans.…”

Robot perception errors and human resolution strategies in situated human–robot dialogue

“…For example, integrating world-knowledge [32] and/or linguistic ontological knowledge [3]; integrating spatial semantics into a compositional/attentional accounts of reference [23,24,31]; learning spatial semantics directly from sensor data using machine learning techniques [12,34]; modelling the functional aspects of spatial semantics in terms of predicting the dynamics of objects in the scene [10,42]; capturing the vagueness and gradation of spatial semantics [17,22,43]; and leveraging analogical reasoning mechanisms to enable agents to apply spatial semantics to new environments [13].…”

Spatial Database Modeling for Indoor Navigation Systems

“…This aspect is emphasized in mentioned OGC document. A good example is also the problem presented in the article (Mast & Wolter, 2013). The problem is related to generating navigation directions.…”