Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GODSAC*: Graph Optimized DSAC* for Robot Relocalization   Deep learning based camera pose estimation from monocular camera images has
seen a recent uptake in Visual SLAM research. Even though such pose estimation
approaches have excellent results in small confined areas like offices and
apartment buildings, they tend to do poorly when applied to larger areas like
outdoor settings, mainly because of the scarcity of distinctive features. We
propose GODSAC* as a camera pose estimation approach that augments pose
predictions from a trained neural network with noisy odometry data through the
optimization of a pose graph. GODSAC* outperforms the state-of-the-art
approaches in pose estimation accuracy, as we demonstrate in our experiments.

22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an