Collect useful sentences in Papers (13)
There have been numerous approaches to the localization problem utilizing different types of sensors [2] and a variety of techniques (e.g.,
[3]–[7]).
The key idea behind most of the current localization
schemes is to optimally combine measurements from proprioceptive sensors that monitor the motion of the vehicle with information collected by exteroceptive sensors that provide a representation of the environment and its signals.
The various approaches to localization differ primarily in the type of estimator
invoked in order to filter out the measurement noise and reduce
the uncertainty associated with the interpretation of the sensor
signals.
Even when a group of, say , robots is considered, the group localization problem is usually resolved by
independently solving pose estimation problems.
On the other hand, a more coordinated schema for localization has a number of advantages that can compensate for
the added complexity
measure a characteristic property of
the area, such as the local vector of the earth’s magnetic field
Properly combining all
this information will result in a single estimate of increased accuracy and reduced uncertainty.
a better estimate
of the position and orientation of a landmark can, in turn, drastically improve the outcome of the robot localization process and
thus, this group will benefit from this collaboration schema.
The advantages stemming from the exchange of information
among the members of a group are more crucial in the case
of heterogeneous robotic colonies.
When a team of robots is
composed of different platforms carrying different proprioceptive and exteroceptive sensors and thus, having different capabilities for self-localization, the quality of the localization estimates will vary significantly across the individual members.
The authors acknowledge that
The authors deal with the problem of exploration of an unknown environment using two mobile robots.
Although practices like those previously mentioned can be
supported within our framework (Sections VI and VII), the advantage of the CL approach is that it provides a solution to
the most general case, in which all the robots in the group can
move simultaneously while continuous visual contact or a map
of the area is not required.
In order to treat the group localization problem, we begin from the reasonable assumptions
The main drawback of addressing the cooperative localization problem as an information combination problem within a single entity (“group organism”) is that it requires centralized
processing and communication.
The CL approach
accomplishes precisely this goal. Since the sensing modalities of
the group are distributed, so too should be the processing modules.
Reference:
Roumeliotis, S. I., & Bekey, G. A. (2002). Distributed multirobot localization. IEEE Transactions on Robotics and Automation, 18(5), 781-795.
[3]–[7]).
The key idea behind most of the current localization
schemes is to optimally combine measurements from proprioceptive sensors that monitor the motion of the vehicle with information collected by exteroceptive sensors that provide a representation of the environment and its signals.
The various approaches to localization differ primarily in the type of estimator
invoked in order to filter out the measurement noise and reduce
the uncertainty associated with the interpretation of the sensor
signals.
Even when a group of, say , robots is considered, the group localization problem is usually resolved by
independently solving pose estimation problems.
On the other hand, a more coordinated schema for localization has a number of advantages that can compensate for
the added complexity
measure a characteristic property of
the area, such as the local vector of the earth’s magnetic field
Properly combining all
this information will result in a single estimate of increased accuracy and reduced uncertainty.
a better estimate
of the position and orientation of a landmark can, in turn, drastically improve the outcome of the robot localization process and
thus, this group will benefit from this collaboration schema.
The advantages stemming from the exchange of information
among the members of a group are more crucial in the case
of heterogeneous robotic colonies.
When a team of robots is
composed of different platforms carrying different proprioceptive and exteroceptive sensors and thus, having different capabilities for self-localization, the quality of the localization estimates will vary significantly across the individual members.
The authors acknowledge that
The authors deal with the problem of exploration of an unknown environment using two mobile robots.
Although practices like those previously mentioned can be
supported within our framework (Sections VI and VII), the advantage of the CL approach is that it provides a solution to
the most general case, in which all the robots in the group can
move simultaneously while continuous visual contact or a map
of the area is not required.
In order to treat the group localization problem, we begin from the reasonable assumptions
The main drawback of addressing the cooperative localization problem as an information combination problem within a single entity (“group organism”) is that it requires centralized
processing and communication.
The CL approach
accomplishes precisely this goal. Since the sensing modalities of
the group are distributed, so too should be the processing modules.
Reference:
Roumeliotis, S. I., & Bekey, G. A. (2002). Distributed multirobot localization. IEEE Transactions on Robotics and Automation, 18(5), 781-795.
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