  Micro Aerial Vehicles (MAVs) that operate in unstructured, unexplored
environments require fast and flexible local planning, which can replan when
new parts of the map are explored. Trajectory optimization methods fulfill
these needs, but require obstacle distance information, which can be given by
Euclidean Signed Distance Fields (ESDFs).
  We propose a method to incrementally build ESDFs from Truncated Signed
Distance Fields (TSDFs), a common implicit surface representation used in
computer graphics and vision. TSDFs are fast to build and smooth out sensor
noise over many observations, and are designed to produce surface meshes.
Meshes allow human operators to get a better assessment of the robot's
environment, and set high-level mission goals.
  We show that we can build TSDFs faster than Octomaps, and that it is more
accurate to build ESDFs out of TSDFs than occupancy maps. Our complete system,
called voxblox, will be available as open source and runs in real-time on a
single CPU core. We validate our approach on-board an MAV, by using our system
with a trajectory optimization local planner, entirely on-board and in
real-time.
