Postprocessing Synchronization of a Laser
Scanning System Aboard a UAV
Marcela do Valle Machado, Antonio Maria Garcia Tommaselli, Fernanda Magri Torres, and Mariana Batista Campos
Abstract
Synchronization of airborne laser scanning devices is a criti-
cal process that directly affects data accuracy. This process
can be more challenging with low-cost airborne laser scanning
(
ALS
) systems because some device connections from off-the-
shelf sensors are less stable. An alternative to synchronization
is performing a postprocessing clock correction. This article
presents a technique for postprocessing synchronization (off-
line) that estimates clock differences based on the correlation
between the signals from the global navigation satellite system
(
GNSS
) trajectory and the light detection and ranging (lidar)
range, followed by refinement with a least-squares method.
The correlation between signals was automatically estimated
considering the planned flight maneuvers, in a flat terrain,
to produce altimetric trajectory variations. Experiments were
performed with an Ibeo
LUX
laser unit integrated with a No-
vAtel
SPAN-IGM-S1
inertial navigation system that was trans-
ported by an unmanned aerial vehicle (
UAV
). The planimetric
and altimetric accuracies of the point cloud obtained with
the proposed postprocessing synchronization technique were
28 cm and 10 cm, respectively, at a flight height of 35 m.
Introduction
An airborne laser scanning (
ALS
) system can be defined as a
combination of a laser ranging measurement (lidar), a scan-
ning unit, and navigation sensors such as global navigation
satellite system (
GNSS
) receivers and an inertial measurement
unit (
IMU
), enabling data collection of three-dimensional point
coordinates of the terrain surface (Wehr and Lohr 1999).
ALS
data can be used for several mapping purposes, including the
generation of digital terrain models, fo
feature extraction, engineering applica
of high-voltage power lines, and cultur
tion (Vosselman and Maas 2010). However,
ALS
is commonly
a high-cost system, which has motivated research on the
development of low-cost alternatives to conventional
ALS
. For
instance, miniaturized laser scanning units and navigation
sensors have recently entered the market and are operable
from small unmanned aerial vehicles (
UAVs
), as presented
by Colomina and Molina (2014). This combination enables
flexible, lightweight, and low-cost data acquisition, especially
compared to manned aircraft systems.
Despite the increase in development of
UAV
laser scanning
(
UAV
-
LS
) systems, some critical issues remain. For instance,
integration and synchronization between decoupled devices
are the main challenge in the use of
UAV
-
LS
, because these
processes directly affect the final data accuracy. The integra-
tion is a technically complex task and it is bounded by the
payload space, preventing the use of further devices. Time
synchronization of the measurement devices is recognized
as a significant source of error because the three-dimensional
coordinates are estimated based on the position and orien-
tation measurements from the navigation system and the
simultaneous distances from the lidar device. The clock dif-
ferences between the
GNSS
receiver and the laser unit can be
minimized in real time by electronic synchronization (online)
or can be estimated with a postprocessing approach (off-line).
Therefore, this work aims to investigate the feasibility of an
off-line synchronization technique for a
UAV
-
LS
system com-
posed of decoupled devices. On this topic, some authors have
evaluated the applicability of
UAV
-
LS
systems and proposed
different solutions to overcome the synchronization challenge
(Jaakkola
et al.
2010; Wallace
et al.
2012; Glennie
et al.
2013;
Kuhnert and Kuhnert 2013; Adler, Xiao and Zhang 2014; Guo
et al.
2017; Torres and Tommaselli 2018).
Jaakkola
et al.
(2010) presented a multi-sensor system
(
FGI
Sensei) with two laser scanning units (Ibeo
LUX
and
Sick LMS151), a
GNSS/IMU
system (NovAtel
SPAN-CPT
), and
additional remote sensors (charge-coupled device camera,
spectrometer, and thermal camera) coupled to a lightweight
UAV
(20–25 kg) to estimate tree heights using a canopy height
model. In this system, the devices were synchronized elec-
tronically using the time stamp recorded in the
GPS
(Global
Positioning System) event marker log, considering a 25-
Hz
signal from the laser scanner and the pulse-per-second sig-
nal from the
GNSS
receiver. A similar
UAV
-lidar system (Ter-
ped by Wallace
et al.
(2012) for forest
beo
LUX
laser scanning unit integrated
(NovAtel
OEMV1-DF
) and an
IMU
(Mi-
croStrain
3DM-GX3
35). The time synchronization between
sensors was performed using an onboard computer (Gumstix
Verdex Pro), which was also used for data logging. Glennie
et al.
(2013) proposed a multi-platform
ALS
system (Velodyne
HDL-32E
lidar scanner, NovAtel
GNSS
receiver, and
OxTS
IMU
)
that could be carried by a
UAV
, in a backpack (for terrestrial
mode), or by small vehicles (quadricycles). Synchronization
between sensors was performed using software developed by
the authors, which combines the platform position and at-
titude with the lidar data obtained by the laser unit. Kuhnert
and Kuhnert (2013) demonstrated a low-weight
ALS
aboard
a
UAV
(Microdrones md4-1000) for high-voltage power-line
monitoring. The
ALS
is composed of a Sick
LD-MRS400001
laser
scanning and
GNSS/IMU
(Microdrones
mdIMU
) system. These
authors noted that laser scanning and
GNSS/IMU
system data
had different data frequencies and thus were interpolated and
resampled in a postprocessing step to associate the platform
Marcela do Valle Machado, Antonio Maria Garcia
Tommaselli, and Mariana Batista Campos are with São
Paulo State University (UNESP), School of Technology and
Sciences, Department of Cartography, São Paulo, Brazil
(
).
Fernanda Magri Torres is with São Paulo State University
(UNESP), School of Technology and Sciences, Department of
Cartography, São Paulo, Brazil; and the Brazilian Institute of
Geography and Statistics (IBGE), Geodesy and Cartography
Division (GGC), Goiânia, Brazil.
Photogrammetric Engineering & Remote Sensing
Vol. 85, No. 10, October 2019, pp. 753–763.
0099-1112/19/753–763
© 2019 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.85.10.753
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
October 2019
753
