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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 performa...
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... drawbacks of the current state-of-the-art algorithms regarding weather are well described in [2]. Table 2 presents the time taken to perform segmentation. Utilizing the Nvidia Jetson Orin, a comparison was made by processing LiDAR point clouds with 128 channels and a horizontal resolution of 2048. ...Similar publications
The perception of autonomous vehicles has to be efficient, robust, and cost-effective. However, cameras are not robust against severe weather conditions, lidar sensors are expensive, and the performance of radar-based perception is still inferior to the others. Camera-radar fusion methods have been proposed to address this issue, but these are cons...
Citations
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.
