  The current optimization approaches of construction machinery are mainly
based on internal sensors. However, the decision of a reasonable strategy is
not only determined by its intrinsic signals, but also very strongly by
environmental information, especially the terrain. Due to the dynamically
changing of the construction site and the consequent absence of a high
definition map, the Simultaneous Localization and Mapping (SLAM) offering the
terrain information for construction machines is still challenging. Current
SLAM technologies proposed for mobile machines are strongly dependent on costly
or computationally expensive sensors, such as RTK GPS and cameras, so that
commercial use is rare. In this study, we proposed an affordable SLAM method to
create a multi-layer gird map for the construction site so that the machine can
have the environmental information and be optimized accordingly. Concretely,
after the machine passes by, we can get the local information and record it.
Combining with positioning technology, we then create a map of the interesting
places of the construction site. As a result of our research gathered from
Gazebo, we showed that a suitable layout is the combination of 1 IMU and 2
differential GPS antennas using the unscented Kalman filter, which keeps the
average distance error lower than 2m and the mapping error lower than 1.3% in
the harsh environment. As an outlook, our SLAM technology provides the
cornerstone to activate many efficiency improvement approaches.
