Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inertial Collaborative Localisation for Autonomous Vehicles using a
  Minimum Energy Filter   Collaborative Localisation has been studied extensively in recent years as a
way to improve pose estimation of unmanned aerial vehicles in challenging
environments. However little attention has been paid toward advancing the
underlying filter design beyond standard Extended Kalman Filter-based
approaches. In this paper, we detail a discrete-time collaborative localisation
filter using the deterministic minimum-energy framework. The filter
incorporates measurements from an inertial measurement unit and models the
effects of sensor bias and gravitational acceleration. We present a simulation
based on real-world vehicle trajectories and IMU data that demonstrates how
collaborative localisation can improve performance over single-vehicle methods.

21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
30
The categories of this paper are: q-alg