IEEE Robotics & Automation Magazine - September 2023 - 35

performance of the target applications. During dynamic scanning
while moving, as odometry estimation using radar data
was challenging, in addition to it not being a common method,
we employed only the 3D lidar used popularly in autonomous
mobile robots. The experiment for dynamic scanning could
explain the extent of influence of the degree of fog or glass on
the odometry-estimation algorithm.
FOGGY ENVIRONMENT
The experiments under foggy conditions were conducted
using two different methods. In the first experiment, lidar
and radar sensors were placed at fixed positions. It was
observed that measurement changes occurred as the fog
thickened. In the second experiment, an odometry-estimation
algorithm was employed to show its degradation due to fog.
SCANNING AT A FIXED POSITION
To simulate the foggy environment, fog was generated for
10 min using a fog machine (UNI-T400) in an enclosed space
sized approximately 3 m × 1.5 m × 3 m. Lidar and radar experiments
were conducted separately but in the same settings.
Based on results in the " Object Detection " section, the sensors
were placed 2 m away from the objects to ensure the best sensing
performance. We also evaluated the environmental robustness
of lidar and radar using wooden and steel rods for reliable
detection. Moreover, considering that radar sensors interfere
with each other, the experiments were conducted individually.
In Figure 6, the measured data are shown to change with
increasing fog. As the fog thickens, the point cloud data from
all of the lidar sensors are significantly affected by virtue of
its scattering property. For a quantitative demonstration, the
point cloud data from lidar are classified into data points at
the region of the rods and in the empty space between the rods
and the sensors. It is observed that the number of the former
tends to decrease over time, while that of the latter increases
due to the fog. Both 3D and 2D mechanical lidar maintained
a certain degree of stability and robustness compared to the
solid-state lidar sensors. Clearly, 3D and 2D mechanical lidar
sensors are less affected by fog than the solid-state lidar sensors,
which cannot recognize objects after 20 s, as shown in
2
3
1
Mobile Robot Path: 12 321
(a)
(b)
(c)
(d)
(e)
FIGURE 5. A comparison of maps generated by three lidar sensors. (a) A ground-truth map, (b) VLP-16, (c) RPLIDAR-A2,
(d) CE30-D, and (e) HPS-3D160.
SEPTEMBER 2023 IEEE ROBOTICS & AUTOMATION MAGAZINE
35

IEEE Robotics & Automation Magazine - September 2023

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - September 2023

Contents
IEEE Robotics & Automation Magazine - September 2023 - Cover1
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IEEE Robotics & Automation Magazine - September 2023 - Cover3
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