Yuan Xingyu, Liu Yu, Xiong Tifan, Zeng Wei, Wang Chao
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China.
Sensors (Basel). 2025 Apr 17;25(8):2526. doi: 10.3390/s25082526.
Common single-line 2D LiDAR sensors and cameras have become core components in the field of robotic perception due to their low cost, compact size, and practicality. However, during the data fusion process, the randomness and complexity of real industrial scenes pose challenges. Traditional calibration methods for LiDAR and cameras often rely on precise targets and can accumulate errors, leading to significant limitations. Additionally, the semantic fusion of LiDAR and camera data typically requires extensive projection calculations, complex clustering algorithms, or sophisticated data fusion techniques, resulting in low real-time performance when handling large volumes of data points in dynamic environments. To address these issues, this paper proposes a semantic fusion algorithm for LiDAR and camera data based on contour and inverse projection. The method has two remarkable features: (1) Combined with the ellipse extraction algorithm of the arc support line segment, a LiDAR and camera calibration algorithm based on various regular shapes of an environmental target is proposed, which improves the adaptability of the calibration algorithm to the environment. (2) This paper proposes a semantic segmentation algorithm based on the inverse projection of target contours. It is specifically designed to be versatile and applicable to both linear and arc features, significantly broadening the range of features that can be utilized in various tasks. This flexibility is a key advantage, as it allows the method to adapt to a wider variety of real-world scenarios where both types of features are commonly encountered. Compared with existing LiDAR point cloud semantic segmentation methods, this algorithm eliminates the need for complex clustering algorithms, data fusion techniques, and extensive laser point reprojection calculations. When handling a large number of laser points, the proposed method requires only one or two inverse projections of the contour to filter the range of laser points that intersect with specific targets. This approach enhances both the accuracy of point cloud searches and the speed of semantic processing. Finally, the validity of the semantic fusion algorithm is proven by field experiments.
常见的单线2D激光雷达传感器和摄像头因其低成本、紧凑尺寸和实用性，已成为机器人感知领域的核心组件。然而，在数据融合过程中，真实工业场景的随机性和复杂性带来了挑战。传统的激光雷达和摄像头校准方法通常依赖精确的目标，且会累积误差，导致显著的局限性。此外，激光雷达和摄像头数据的语义融合通常需要大量的投影计算、复杂的聚类算法或复杂的数据融合技术，在动态环境中处理大量数据点时，实时性能较低。为了解决这些问题，本文提出了一种基于轮廓和逆投影的激光雷达和摄像头数据语义融合算法。该方法有两个显著特点：（1）结合弧支撑线段的椭圆提取算法，提出了一种基于环境目标各种规则形状的激光雷达和摄像头校准算法，提高了校准算法对环境的适应性。（2）本文提出了一种基于目标轮廓逆投影的语义分割算法。它特别设计得具有通用性，适用于线性和弧形特征，显著拓宽了可在各种任务中利用的特征范围。这种灵活性是一个关键优势，因为它使该方法能够适应更广泛的现实世界场景，在这些场景中这两种特征都很常见。与现有的激光雷达点云语义分割方法相比，该算法无需复杂的聚类算法、数据融合技术和大量的激光点重投影计算。在处理大量激光点时，该方法仅需对轮廓进行一两次逆投影，即可过滤与特定目标相交的激光点范围。这种方法提高了点云搜索的准确性和语义处理的速度。最后，通过现场实验证明了语义融合算法的有效性。
