FIGURE 5 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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The environment in which the training and testing LiDAR data was collected. The MATLAB LiDAR labeler app was used to label the dust points seen in red.
Source publication
Light detection and ranging (LiDAR) sensors can create high-quality scans of an environment. However, LiDAR point clouds are affected by harsh weather conditions since airborne particles are easily detected. In literature, conventional filtering and artificial intelligence (AI) filtering methods have been used to detect, and remove, airborne partic...
Contexts in source publication
Context 1
... of active leaf blowers was changed for each test run. Meaning that each test run had 1 leaf blower active, then an additional leaf blower was activated to create the dense dust cloud. Also, the distance of the dynamic nondust obstacle was varied as it moved through the environment. An example of the data collection environment can be seen in Fig. 5. To label the LiDAR data collected, the LiDAR labeller app in MATLAB was used ...
Context 2
... seen in Fig. 5, only the data directly in front of the sensor was considered. There are two reasons for this. The first reason is that the dust clouds were generated only in front of the sensor. If the full field of view of the LiDAR sensor was considered, a significant amount of non-dust particles would be captured from behind the sensor. If this ...
Citations
... The first focuses on point cloud segmentation and object detection and recognition using LiDAR. The second area aims to establish the robustness of LiDAR point clouds against weather and external environmental factors [4,5]. ...
... These techniques utilize the geometry and light intensity of the point cloud to effectively eliminate outlier points that resemble airborne particles. However, a drawback of the method is that it removes object points, which can lead to slower algorithm performance when attempting to restore the point clouds using conventional methods [5]. ...
The paper presents a novel method for real-time detection of off-road drivable regions in LiDAR point clouds without relying on deep learning. Our method directly distinguishes drivable regions while accounting for noise caused by snow and fog. By focusing on the scanning characteristics of rotating LiDAR, our PIIC approach achieves robust performance in challenging off-road environments. The proposed technique divides the space into sections and segments drivable areas by counting points in each section. It allows noise influence to be countable using a simple probabilistic model. The developed solution offers significant advancements in autonomous driving in off-road environments since the method offers drivability in real-time with noise immunity. Experimental results demonstrate the efficiency of the proposed method in hazardous situations such as heavy snow, fog, and crosstalk from other LiDAR signals. Importantly, the low computational load enables real-time operation and provides promising opportunities for fusion with other recognition methods.
Although Light Detection and Ranging (LiDAR) is a sensor type for autonomous vehicles, it is recognized as an essential tool in various fields, such as drones, Unmanned Surface Vehicles (USVs), Unmanned Ground Vehicles (UGVs), and surveillance facilities. This is because LiDAR has advantages including a high resolution, 360-degree sensing capabilities, and consistent performance during the day and at night. However, LiDAR has the fatal disadvantage of performing poorly in snow, rain, and fog. Therefore, studies are attempting to remove noise points caused by snow, rain, and fog to ensure that LiDAR performs well even under extreme weather conditions. These studies began in approximately 2020, coinciding with accelerated research on autonomous driving, and they are still being actively pursued today. In particular, with the development of artificial intelligence technology in addition to the existing conventional methods, various approaches are being developed, but papers that thoroughly analyze and organize this research have not yet been published. Accordingly, in this study, we aim to comprehensively review the studies that have attempted to remove LiDAR sensor noise under extreme weather conditions through various methods. This review discusses the latest LiDAR denoising algorithms, with a particular focus on techniques implemented under snowy conditions. These algorithms can be categorized into five types: distance-based, intensity-based, fusion-based, learning-based, and other methods. In addition, this paper covers extreme weather datasets and related perception studies.
