Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving the Accuracy of Stereo Visual Odometry Using Visual
  Illumination Estimation   In the absence of reliable and accurate GPS, visual odometry (VO) has emerged
as an effective means of estimating the egomotion of robotic vehicles. Like any
dead-reckoning technique, VO suffers from unbounded accumulation of drift error
over time, but this accumulation can be limited by incorporating absolute
orientation information from, for example, a sun sensor. In this paper, we
leverage recent work on visual outdoor illumination estimation to show that
estimation error in a stereo VO pipeline can be reduced by inferring the sun
position from the same image stream used to compute VO, thereby gaining the
benefits of sun sensing without requiring a dedicated sun sensor or the sun to
be visible to the camera. We compare sun estimation methods based on
hand-crafted visual cues and Convolutional Neural Networks (CNNs) and
demonstrate our approach on a combined 7.8 km of urban driving from the popular
KITTI dataset, achieving up to a 43% reduction in translational average root
mean squared error (ARMSE) and a 59% reduction in final translational drift
error compared to pure VO alone.

23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph