@InProceedings{Walters19,
author = {Walters, Celyn and Mendez, Oscar and Hadfield, Simon and Bowden, Richard},
year = {2019},
month = {10},
pages = {},
title = {A Robust Extrinsic Calibration Framework for Vehicles with Unscaled Sensors},
  Publisher                = {IEEE/RSJ},
  Booktitle                = {Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  
  Abstract                 = {Accurate extrinsic sensor calibration is essential
for both autonomous vehicles and robots. Traditionally this is
an involved process requiring calibration targets, known fiducial
markers and is generally performed in a lab. Moreover, even a
small change in the sensor layout requires recalibration. With
the anticipated arrival of consumer autonomous vehicles, there
is demand for a system which can do this automatically, after
deployment and without specialist human expertise.
To solve these limitations, we propose a flexible framework
which can estimate extrinsic parameters without an explicit
calibration stage, even for sensors with unknown scale. Our
first contribution builds upon standard hand-eye calibration
by jointly recovering scale. Our second contribution is that
our system is made robust to imperfect and degenerate sensor
data, by collecting independent sets of poses and automatically
selecting those which are most ideal.
We show that our approach’s robustness is essential for the
target scenario. Unlike previous approaches, ours runs in real
time and constantly estimates the extrinsic transform. For both
an ideal experimental setup and a real use case, comparison
against these approaches shows that we outperform the state-of-
the-art. Furthermore, we demonstrate that the recovered scale
may be applied to the full trajectory, circumventing the need
for scale estimation via sensor fusion.},
  %Comment                  = {},
  Url                      = {http://personalpages.surrey.ac.uk/s.hadfield/papers/Walters19.pdf},
}