Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing Drift in Visual Odometry by Inferring Sun Direction Using a
  Bayesian Convolutional Neural Network   We present a method to incorporate global orientation information from the
sun into a visual odometry pipeline using only the existing image stream, where
the sun is typically not visible. We leverage recent advances in Bayesian
Convolutional Neural Networks to train and implement a sun detection model that
infers a three-dimensional sun direction vector from a single RGB image.
Crucially, our method also computes a principled uncertainty associated with
each prediction, using a Monte Carlo dropout scheme. We incorporate this
uncertainty into a sliding window stereo visual odometry pipeline where
accurate uncertainty estimates are critical for optimal data fusion. Our
Bayesian sun detection model achieves a median error of approximately 12
degrees on the KITTI odometry benchmark training set, and yields improvements
of up to 42% in translational ARMSE and 32% in rotational ARMSE compared to
standard VO. An open source implementation of our Bayesian CNN sun estimator
(Sun-BCNN) using Caffe is available at https://github.
com/utiasSTARS/sun-bcnn-vo

20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex