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使用激光雷达环的边缘触发三维目标检测

Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring.

作者信息

Song Eunji, Jeong Seyoung, Hwang Sung-Ho

机构信息

Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Republic of Korea.

出版信息

Sensors (Basel). 2024 Mar 21;24(6):2005. doi: 10.3390/s24062005.

DOI:10.3390/s24062005
PMID:38544267
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10974441/
Abstract

Autonomous driving recognition technology that can quickly and accurately recognize even small objects must be developed in high-speed situations. This study proposes an object point extraction method using rule-based LiDAR ring data and edge triggers to increase both speed and performance. The LiDAR's ring information is interpreted as a digital pulse to remove the ground, and object points are extracted by detecting discontinuous edges of the z value aligned with the ring ID and azimuth. A bounding box was simply created using DBSCAN and PCA to check recognition performance from the extracted object points. Verification of the results of removing the ground and extracting points through Ring Edge was conducted using SemanticKITTI and Waymo Open Dataset, and it was confirmed that both F1 scores were superior to RANSAC. In addition, extracting bounding boxes of objects also showed higher PDR index performance when verified in open datasets, virtual driving environments, and actual driving environments.

摘要

必须开发能够在高速情况下快速准确识别甚至小物体的自动驾驶识别技术。本研究提出了一种使用基于规则的激光雷达环数据和边缘触发的目标点提取方法，以提高速度和性能。激光雷达的环信息被解释为数字脉冲以去除地面，通过检测与环ID和方位角对齐的z值的不连续边缘来提取目标点。使用DBSCAN和PCA简单地创建一个边界框，以从提取的目标点检查识别性能。使用SemanticKITTI和Waymo开放数据集对通过环边缘去除地面和提取点的结果进行了验证，并且证实两个F1分数均优于RANSAC。此外，在开放数据集、虚拟驾驶环境和实际驾驶环境中进行验证时，提取物体的边界框也显示出更高的PDR指数性能。

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