Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time Semantic 3D Dense Occupancy Mapping with Efficient Free Space
  Representations   A real-time semantic 3D occupancy mapping framework is proposed in this
paper. The mapping framework is based on the Bayesian kernel inference strategy
from the literature. Two novel free space representations are proposed to
efficiently construct training data and improve the mapping speed, which is a
major bottleneck for real-world deployments. Our method achieves real-time
mapping even on a consumer-grade CPU. Another important benefit is that our
method can handle dynamic scenarios, thanks to the coverage completeness of the
proposed algorithm. Experiments on real-world point cloud scan datasets are
presented.

14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
19
The categories of this paper are: eess