  Environment mapping is an essential prerequisite for mobile robots to perform
different tasks such as navigation and mission planning. With the availability
of low-cost 2D LiDARs, there are increasing applications of such 2D LiDARs in
industrial environments. However, environment mapping in an unknown and
feature-less environment with such low-cost 2D LiDARs remains a challenge. The
challenge mainly originates from the short-range of LiDARs and complexities in
performing scan matching in these environments. In order to resolve these
shortcomings, we propose to fuse the ultra-wideband (UWB) with 2D LiDARs to
improve the mapping quality of a mobile robot. The optimization-based approach
is utilized for the fusion of UWB ranging information and odometry to first
optimize the trajectory. Then the LiDAR-based loop closures are incorporated to
improve the accuracy of the trajectory estimation. Finally, the optimized
trajectory is combined with the LiDAR scans to produce the occupancy map of the
environment. The performance of the proposed approach is evaluated in an indoor
feature-less environment with a size of 20m*20m. Obtained results show that the
mapping error of the proposed scheme is 85.5% less than that of the
conventional GMapping algorithm with short-range LiDAR (for example Hokuyo
URG-04LX in our experiment with a maximum range of 5.6m).
