  Euclidean Signed Distance Field (ESDF) is useful for online motion planning
of aerial robots since it can easily query the distance and gradient
information against obstacles. Fast incrementally built ESDF map is the
bottleneck for conducting real-time motion planning. In this paper, we
investigate this problem and propose a mapping system called FIESTA to build
global ESDF map incrementally. By introducing two independent updating queues
for inserting and deleting obstacles separately, and using Indexing Data
Structures and Doubly Linked Lists for map maintenance, our algorithm updates
as few as possible nodes using a BFS framework. Our ESDF map has high
computational performance and produces near-optimal results. We show our method
outperforms other up-to-date methods in term of performance and accuracy by
both theory and experiments. We integrate FIESTA into a completed quadrotor
system and validate it by both simulation and onboard experiments. We release
our method as open-source software for the community.
