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Explorer
Development
Year 1
The emphasis for the first year was on integrating some of the basic
building blocks to get an integrated system that could build a map of the
environment, navigate through it either by following a user or driving
autonomously to a specified goal location and interaction with the user
using spoken dialogue.
The scenarios included clarification dialogue, annotion of the map using
natural language, verification, going to places.
Year 2
During the second year the robot the laser based place classification
algorithm was integrated into the system. Furthermore, simple object
recognition functionality was added. These two combined allowed the robot
to infer knowledge and reason about space to a larger exend. Initial work
on situation awareness was also integrated into the system so that the
robot could adapt its person following behaviour when passing though
doors.
Year 3
In the third year, methods for improving object recognition were
investigated. View planning based on a map of the environment was used to
more efficiently cover the space during search. In order to allow for
smaller objects to be detected with a relatively low resolution camera
the object recognition process was divided into two parts, one for
detection of objects and one for recognition. In the detection phase
object hypotheses are formed and these are investigated by gradually
zooming in on the objects. When the object fills enough of the image
recognition is performed. In addition, an enhanced visual distance
estimate was implemented. Initial work was also started towards a more
general, hierarchical SLAM framework along with an improved navigation
graph.
We have also worked on an approach to intelligent, interactive people
following for autonomous robots. The approach combines robust methods for
simultaneous localization and mapping and for people tracking in order to
yield a socially and environmentally sensitive people following behavior.
Unlike current purely reactive approaches ("nearest point following") it
enables the robot to follow a human in a socially acceptable way,
providing verbal and non-verbal feedback to the user where necessary. At
the same time, the robot makes use of information about the spatial and
functional organization of its environment, so that it can anticipate
likely actions performed by a human, and adjust its motion accordingly.
As a result, the robot's behaviors become less reactive and more
intuitive when following people around an indoor environment. Below you
can find two videos that contrast a purely reactive approach and our
situation-aware approach in a corridor setting.
Year 4
During the fourth year we combined the work on laser based place
classification with the work on vision based place recognition. A series
of experiments clearly showed the benefits of combining multiple cues
from multiple sensors. Towards the end of the year the vision based work
was extended from recognition to classification and combined with the
laser counter part formed a unified semantic place labeling sub-system in
the Explorer system.
Much of the effort in the Explorer was spent on getting a complete
integration with the CAST framework and thus be able to join the two
demonstrators (PlayMate and Explorer) in terms of a common software
platform. The two demonstrator in the end shared the underlying
framework and a large portion of the components. The control paradigm
using the MAPSIM continual planner and the binder primarily developed
within the context of the PlayMate system was adapted which allowed for a
much more flexible and modular way of control of the explorer system.
Through the use of the binder cross-modal information could be used by
the planner to run the show. A large part of this work was in creating a
stable abstraction for the information available in the spatial model,
suitable for the binder and the planner to operate on.
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