  In this paper, we develop an online active mapping system to enable a
quadruped robot to autonomously survey large physical structures. We describe
the perception, planning and control modules needed to scan and reconstruct an
object of interest, without requiring a prior model. The system builds a voxel
representation of the object, and iteratively determines the Next-Best-View
(NBV) to extend the representation, according to both the reconstruction itself
and to avoid collisions with the environment. By computing the expected
information gain of a set of candidate scan locations sampled on the as-sensed
terrain map, as well as the cost of reaching these candidates, the robot
decides the NBV for further exploration. The robot plans an optimal path
towards the NBV, avoiding obstacles and un-traversable terrain. Experimental
results on both simulated and real-world environments show the capability and
efficiency of our system. Finally we present a full system demonstration on the
real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade
and an industrial structure.
