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Mobile Robot Localization
effectively blind when a group of human visitors completely surround the robot. This is
because its map contains only environmental features that are, at that point, fully hidden
from the robot’s sensors by the wall of people. In the best case, the robot should recognize
its occlusion and make no effort to localize using these invalid sensor readings. In the worst
case, the robot will localize with the fully occluded data, and will update its location incor-
rectly. A vision sensor that can discriminate the local conditions of the robot (e.g,. we are
surrounded by people) can help eliminate this error mode.
A second open challenge in mobile robot localization involves the traversal of open
spaces. Existing localization techniques generally depend on local measures such as range,
thereby demanding environments that are somewhat densely filled with objects that the
sensors can detect and measure. Wide-open spaces such as parking lots, fields of grass, and
indoor atriums such as those found in convention centers pose a difficulty for such systems
because of their relative sparseness. Indeed, when populated with humans, the challenge is
exacerbated because any mapped objects are almost certain to be occluded from view by
the people.
Once again, more recent technologies provide some hope of overcoming these limita-
tions. Both vision and state-of-the-art laser rangefinding devices offer outdoor performance
with ranges of up to a hundred meters and more. Of course, GPS performs even better. Such
long-range sensing may be required for robots to localize using distant features.
This trend teases out a hidden assumption underlying most topological map representa-
tions. Usually, topological representations make assumptions regarding spatial locality: a
node contains objects and features that are themselves within that node. The process of map
creation thus involves making nodes that are, in their own self-contained way, recognizable
by virtue of the objects contained within the node. Therefore, in an indoor environment,
each room can be a separate node, and this is reasonable because each room will have a
layout and a set of belongings that are unique to that room.
However, consider the outdoor world of a wide-open park. Where should a single node
end and the next node begin? The answer is unclear because objects that are far away from
the current node, or position, can yield information for the localization process. For exam-
ple, the hump of a hill at the horizon, the position of a river in the valley, and the trajectory
of the sun all are nonlocal features that have great bearing on one’s ability to infer current
position. The spatial locality assumption is violated and, instead, replaced by a visibility
criterion: the node or cell may need a mechanism for representing objects that are measur-
able and visible from that cell. Once again, as sensors improve and, in this case, as outdoor
locomotion mechanisms improve, there will be greater urgency to solve problems associ-
ated with localization in wide-open settings, with and without GPS-type global localization
sensors.
We end this section with one final open challenge that represents one of the fundamental
academic research questions of robotics: sensor fusion. A variety of measurement types are