  Occupancy mapping has been widely utilized to represent the surroundings for
autonomous robots to perform tasks such as navigation and manipulation. While
occupancy mapping in 2-D environments has been well-studied, there have been
few approaches suitable for 3-D dynamic occupancy mapping which is essential
for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping
algorithm called DS-K3DOM. We first establish a Bayesian method to sequentially
update occupancy maps for a stream of measurements based on the random finite
set theory. Then, we approximate it with particles in the Dempster-Shafer
domain to enable real-time computation. Moreover, the algorithm applies
kernel-based inference with Dirichlet basic belief assignment to enable dense
mapping from sparse measurements. The efficacy of the proposed algorithm is
demonstrated through simulations and real experiments.
