IEEE Robotics & Automation Magazine - June 2020 - 148

generated by the proposed place description network. Furthermore, in the loop-closure result, the red and green point clouds
are selected as one of the loop-closure candidate pairs since
these two point clouds have the smallest L 2 distance between
their corresponding place descriptors. Then we calculate their
relative pose by the proposed loop-closure method and shift
the green point cloud according to the obtained relative pose to
generate the blue one, which shows the final point cloud registration result.
The detailed system performance is summarized in
TableĀ 1, which validates that the proposed systems are able to
provide sufficient performance for automated cargo transportation tasks. Figure 7(c)-(g) shows some real autonomous
transportation scenarios to demonstrate the practical applicability of the proposed system. Detailed planning and control
approaches can be found in [20].
In Table 2, we compare the proposed loop-closure detection
approach presented in the section "Deep Learning-Enhanced
Large-Scale Mapping and Localization" with the state-of-theart laser-based SLAM system LeGO-LOAM [7] in the airport
environment shown in Figure 7(a) and (b) (within the red
area). To ensure fairness, we do not use local sequence matching in the proposed approach. Table 2 shows that the number
of true-positive (TP), false-positive (FP), false-negative (FN),
and true-negative (TN) results for each approach along with
recall index (TP/(TP + FN)). From Table 2, we find that the

accuracy (recall) of the proposed approach is much higher than
LeGO-LOAM since the proposed approach can detect more
correct loop-closure results (TP) and has a much lower omission rate (FN).
In Figure 10, we compare the mapping performance of
the proposed approach presented in the section "Deep
Learning-Enhanced Large-Scale Mapping and Localization" with the state-of-the-art laser-based approach LeGOLOAM [7] and the visual-based approach ORB-SLAM2
[6]. In Figure 10(a), ORB-SLAM2 fails to perform visual
odometry tasks due to the absence of enough feature points
(caused by motion blur in turnings and large and rapid
light condition variations in the process of indoor/outdoor
switches). In Figure 10(b), LeGO-LOAM also fails to build
a loop-closed map due to the nonrobustness of feature
matchings and large accumulating errors over a long distance. However, in Figure 10(c) and (d), the proposed
approach successfully builds the loop-closed map of the
whole terminal environment, which shows the advanced
performance of the proposed approach in large-scale
dynamic environments.
In the future, we will implement a group of autonomous
vehicles working 24 h/day. The successful application of the
proposed automated transportation system will greatly
improve the efficiency and quality of cargo transportation at
the Hong Kong International Airport, thus leading to revolutionary changes.
Conclusions
This article proposes a system-level overview of advanced
localization and perception approaches for autonomous
industrial vehicles. In particular, deep learning techniques
are utilized to enhance system performance in a large-scale
industrial environment. The proof-of-concept vehicle has
been successfully applied in the Hong Kong International
Airport to deliver cargo autonomously. This is the first
time automated cargo transportation has been achieved at
the Hong Kong International Airport, thus reaching a
major milestone.
Enhanced by the proposed deep learning-based, largescale place description network, the proposed localization
system is able to achieve place recognition and loop-closure
detection tasks in large-scale dynamic environments with
sparse static information, Furthermore, it is the first time that
we solve the point cloud registration problem by introducing
the learning-based scene flow prediction method, thus making it possible to achieve the loop-closure task in the absence
of initial pose estimation. In the industry environment,

Table 1. The performance of the proposed
autonomous vehicle system.
Parameter

Value

Maximum velocity

20 km/h

Maximum payload

50 t

Maximum continuous working duration

24 h

Perception range

50 m

Maximum number of simultaneously tracked objects

10

Perception frequency

10 Hz

Loop-closure detection frequency in mapping

2 Hz

Repetitive localization accuracy

5 cm

Localization frequency

10 Hz

Maximum velocity control error

10%

Path-tracking accuracy (straight-line tracking)

8 cm

Path-tracking accuracy (turning)

12 cm

Storage-bin docking accuracy

5 cm

Table 2. Comparison results of loop-closure detection in airport environments
(with different distance bounds in defining correct loop-closure detection results: 5 m/2.5 m/1 m).

148

*

TP

FP

FN

TN

Recall

Our results

255/224/171

0/0/0

10/9/7

754/786/841

0.962/0.961/0.961

LeGO-LOAM

97/97/97

0/0/0

168/136/81

754/786/841

0.366/0.416/0.545

IEEE ROBOTICS & AUTOMATION MAGAZINE

*

JUNE 2020



IEEE Robotics & Automation Magazine - June 2020

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