Authors: Xun S. Zhou and Stergios I. Roumeliotis
Abstract:
This paper presents a new approach to the multi-
robot map-alignment problem that enables teams of robots to
build joint maps without initial knowledge of their relative poses.
The key contribution of this work is an optimal algorithm for
merging (not necessarily overlapping) maps that are created
by different robots independently. Relative pose measurements
between pairs of robots are processed to compute the coordinate
transformation between any two maps. Noise in the robot-
to-robot observations, propagated through the map-alignment
process, increases the error in the position estimates of the
transformed landmarks, and reduces the overall accuracy of
the merged map. When there is overlap between the two maps,
landmarks that appear twice provide additional information, in
the form of constraints, which increases the alignment accuracy.
Landmark duplicates are identified through a fast nearest-
neighbor matching algorithm. In order to reduce the compu-
tational complexity of this search process, a kd-tree is used
to represent the landmarks in the original map. The criterion
employed for matching any two landmarks is the Mahalanobis
distance. As a means of validation, we present experimental
results obtained from two robots mapping an area of 4,800 m
2.
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