Citation: Le, M.-H.; Cheng, C.-H.;
Liu, D.-G.; Nguyen, T.-T. An
Adaptive Group of Density Outlier
Removal Filter: Snow Particle
Removal from LiDAR Data.
Electronics 2022, 11, 2993. https://
doi.org/10.3390/electronics11192993
Academic Editors: Nunzio Cennamo,
Subhas Mukhopadhyay, Yangquan
Chen, M. Jamal Deen, Junseop Lee
and Simone Morais
Received: 9 August 2022
Accepted: 16 September 2022
Published: 21 September 2022
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Article
An Adaptive Group of Density Outlier Removal Filter: Snow
Particle Removal from LiDAR Data
Minh-Hai Le
1,2,
* , Ching-Hwa Cheng
3
, Don-Gey Liu
1,3
and Thanh-Tuan Nguyen
1
1
Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung 40724, Taiwan
2
Department of Electrical and Electronics, Tra Vinh University, Tra Vinh 87000, Vietnam
3
Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan
* Correspondence: lmhai@tvu.edu.tw
Abstract:
Light Detection And Ranging (LiDAR) is an important technology integrated into self-
driving cars to enhance the reliability of these systems. Even with some advantages over cameras,
it is still limited under extreme weather conditions such as heavy rain, fog, or snow. Traditional
methods such as Radius Outlier Removal (ROR) and Statistical Outlier Removal (SOR) are limited
in their ability to detect snow points in LiDAR point clouds. This paper proposes an Adaptive
Group of Density Outlier Removal (AGDOR) filter that can remove snow particles more effectively
in raw LiDAR point clouds, with verification on the Winter Adverse Driving Dataset (WADS). In
our proposed method, an intensity threshold combined with a proposed outlier removal filter was
employed. Outstanding performance was obtained, with higher accuracy up to 96% and processing
speed of
0.51 s
per frame in our result. In particular, our filter outperforms the state-of-the-art filter
by achieving a 16.32% higher Precision at the same accuracy. However, our method archive is lower
in recall than the state-of-the-art method. This clearly indicates that AGDOR retains a significant
amount of object points from LiDAR. The results suggest that our filter would be useful for snow
removal under harsh weathers for autonomous driving systems.
Keywords: self-driving car; LiDAR point clouds; intensity filter; snow removal
1. Introduction
Along with the strong development of the auto industry, increasingly, self-driving cars
appear to support and ensure the safety of drivers. Vehicles will be able to recognize and
warn of surrounding obstacles to reduce accidents and protect people. Modern autonomous
vehicle systems often use a combination of multiple sensors to limit the risk of accidents [
1
].
In this case, processing the input data from the sensors is especially important. These two
abilities—planning the destination to move and avoiding obstacles—help autonomous
vehicles find the proper decision. Sensors are combined to reduce blind spots and enhance
vehicle visibility [
2
–
4
]. Cameras and radar are often used as primary sensors to collect
information for autonomous vehicles [
5
–
7
]. Recently, Light Detection And Ranging (LiDAR)
has also been integrated into self-driving cars to enhance system reliability. It is also
considered as the eye in vehicles, as it can provide enough information around the vehicle
and fill the missing points of other sensors. The combination of multiple sensors can
solve considerable problems for autonomous vehicles such as 3D object recognition, lane
detection, and positioning [3,8,9].
Currently, LiDAR is one of the most widely utilized optical remote sensing techniques
for autonomous driving systems [
10
]. Many high-level autonomous vehicles use LiDAR as
one of their primary sensors. For an autonomous vehicle, LiDAR is used for detection and
localization. However, the resolution of LiDAR is limited [
11
]. Therefore, compatibility with
cameras will overcome the weaknesses of LiDAR in recognition [
12
,
13
]. The combination of
LiDAR and other sensors will increase the reliability of the system, allowing an autonomous
vehicle to move smoothly and avoid other collisions.
Electronics 2022, 11, 2993. https://doi.org/10.3390/electronics11192993 https://www.mdpi.com/journal/electronics
