Semantic Navigation for Robot-Human Teams

Most of the work done in localization, mapping, and navigation for both ground and aerial vehicles has been done by means of point landmarks or occupancy grids, using vision or laser range finders. However, to make these robots one day able to cooperate with humans in complex scenarios, we need to build semantic maps of the environment.

This video shows a particle-filter-map-based localization using "soft" object detection. Soft object detection differs from "hard" object detection in that we do not extract an "affirmative/negative" response about the presence of the object but rather we compute, for each pixel in the current frame, the probability that the object under consideration is there. This gives raise to many false positive (see the multiple peaks in the object "heat-map") that are disambiguated during motion by the particle filter.

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