Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving drone localisation around wind turbines using monocular
  model-based tracking   We present a novel method of integrating image-based measurements into a
drone navigation system for the automated inspection of wind turbines. We take
a model-based tracking approach, where a 3D skeleton representation of the
turbine is matched to the image data. Matching is based on comparing the
projection of the representation to that inferred from images using a
convolutional neural network. This enables us to find image correspondences
using a generic turbine model that can be applied to a wide range of turbine
shapes and sizes. To estimate 3D pose of the drone, we fuse the network output
with GPS and IMU measurements using a pose graph optimiser. Results illustrate
that the use of the image measurements significantly improves the accuracy of
the localisation over that obtained using GPS and IMU alone.

12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex