Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mapless Online Detection of Dynamic Objects in 3D Lidar   This paper presents a model-free, setting-independent method for online
detection of dynamic objects in 3D lidar data. We explicitly compensate for the
moving-while-scanning operation (motion distortion) of present-day 3D spinning
lidar sensors. Our detection method uses a motion-compensated freespace
querying algorithm and classifies between dynamic (currently moving) and static
(currently stationary) labels at the point level. For a quantitative analysis,
we establish a benchmark with motion-distorted lidar data using CARLA, an
open-source simulator for autonomous driving research. We also provide a
qualitative analysis with real data using a Velodyne HDL-64E in driving
scenarios. Compared to existing 3D lidar methods that are model-free, our
method is unique because of its setting independence and compensation for
pointcloud motion distortion.

32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th