  In order to obtain a compact line segment-based map representation for
localization and planning of mobile robots, it is necessary to merge redundant
line segments which physically represent the same part of the environment in
different scans. In this paper, a consistent and efficient redundant line
segment merging approach (CAE-RLSM) is proposed for online feature map
building. The proposed CAE-RLSM is composed of two newly proposed modules:
one-to-many incremental line segment merging (OTM-ILSM) and multi-processing
global map adjustment (MP-GMA). Different from state-of-the-art offline merging
approaches, the proposed CAE-RLSM can achieve real-time mapping performance,
which not only reduces the redundancy of incremental merging with high
efficiency, but also solves the problem of global map adjustment after loop
closing to guarantee global consistency. Furthermore, a new correlation-based
evaluation metric is proposed for the quality evaluation of line segment maps.
This evaluation metric does not require manual measurement of the environmental
metric information, instead it makes full use of globally consistent laser
scans obtained by simultaneous localization and mapping (SLAM) systems to
compare the performance of different line segment-based mapping approaches in
an objective and fair manner. Comparative experimental results with respect to
a mean shift-based offline redundant line segment merging approach (MS-RLSM)
and an offline version of one-to-one incremental line segment merging approach
(O$^2$TO-ILSM) on both public data sets and self-recorded data set are
presented to show the superior performance of CAE-RLSM in terms of efficiency
and map quality in different scenarios.
