Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Inertial Localization   This paper proposes the inertial localization problem, the task of estimating
the absolute location from a sequence of inertial sensor measurements. This is
an exciting and unexplored area of indoor localization research, where we
present a rich dataset with 53 hours of inertial sensor data and the associated
ground truth locations. We developed a solution, dubbed neural inertial
localization (NILoc) which 1) uses a neural inertial navigation technique to
turn inertial sensor history to a sequence of velocity vectors; then 2) employs
a transformer-based neural architecture to find the device location from the
sequence of velocities. We only use an IMU sensor, which is energy efficient
and privacy preserving compared to WiFi, cameras, and other data sources. Our
approach is significantly faster and achieves competitive results even compared
with state-of-the-art methods that require a floorplan and run 20 to 30 times
slower. We share our code, model and data at https://sachini.github.io/niloc.

17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc