  We propose a scheme called MuNES for single mapping and trajectory planning
including elevators and stairs. Optimized multifloor trajectories are important
for optimal interfloor movements of robots. However, given two or more options
of moving between floors, it is difficult to select the best trajectory because
there are no suitable indoor multifloor maps in the existing methods. To solve
this problem, MuNES creates a single multifloor map including elevators and
stairs by estimating altitude changes based on pressure data. In addition, the
proposed method performs floor-based loop detection for faster and more
accurate loop closure. The single multifloor map is then voxelized leaving only
the parts needed for trajectory planning. An optimal and realistic multifloor
trajectory is generated by exploring the voxels using an A* algorithm based on
the proposed cost function, which affects realistic factors. We tested this
algorithm using data acquired from around a campus and note that a single
accurate multifloor map could be created. Furthermore, optimal and realistic
multifloor trajectory could be found by selecting the means of motion between
floors between elevators and stairs according to factors such as the starting
point, ending point, and elevator waiting time. The code and data used in this
work are available at https://github.com/donghwijung/MuNES.
