The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

中文翻译：

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基于视觉-二维激光雷达数据融合的林区非结构化道路检测与建模

森林环境中非结构化道路的检测和识别对于智能林业技术至关重要。森林道路缺乏有效的参照物和人工标志，非线性度和不确定性高，给森林工程车辆带来了严峻的挑战。本研究旨在提高林业工程的自动化和智能化，提出一种基于图像处理与二维激光雷达检测相结合的非结构化道路检测与识别方法。该方法使用“改进的种子+支持向量机（SVM）”策略对图像中的道路区域进行快速分类识别。结合二维激光雷达点云数据在图像上的重映射，充分考虑了森林无人导航车的实际导航需求，并实现了基于车辆坐标系的道路模型构建。将该算法移植到自建的智能导航平台上，验证了其可行性和有效性。实验结果表明，在低速条件下，系统能够满足平均10帧/秒处理数据的实时性要求。对于道路模型的中心线，图像与激光雷达的匹配误差不超过0.119 m。该算法可为林区非结构化道路的识别提供有效支持。该技术对林业工程车辆在自主巡检喷药、苗木收割、打滑、运输等方面具有重要应用价值。将该算法移植到自建的智能导航平台上，验证了其可行性和有效性。实验结果表明，在低速条件下，系统能够满足平均10帧/秒处理数据的实时性要求。对于道路模型的中心线，图像与激光雷达的匹配误差不超过0.119 m。该算法可为林区非结构化道路的识别提供有效支持。该技术对林业工程车辆在自主巡检喷药、苗木收割、打滑、运输等方面具有重要应用价值。将该算法移植到自建的智能导航平台上，验证了其可行性和有效性。实验结果表明，在低速条件下，系统能够满足平均10帧/秒处理数据的实时性要求。对于道路模型的中心线，图像与激光雷达的匹配误差不超过0.119 m。该算法可为林区非结构化道路的识别提供有效支持。该技术对林业工程车辆在自主巡检喷药、苗木收割、打滑、运输等方面具有重要应用价值。系统可满足平均10帧/秒处理数据的实时性要求。对于道路模型的中心线，图像与激光雷达的匹配误差不超过0.119 m。该算法可为林区非结构化道路的识别提供有效支持。该技术对林业工程车辆在自主巡检喷药、苗木收割、打滑、运输等方面具有重要应用价值。系统可满足平均10帧/秒处理数据的实时性要求。对于道路模型的中心线，图像与激光雷达的匹配误差不超过0.119 m。该算法可为林区非结构化道路的识别提供有效支持。该技术对林业工程车辆在自主巡检喷药、苗木收割、打滑、运输等方面具有重要应用价值。