| 摘 要： |
Robust rock and pothole detection is essential for autonomous exploration in challenging desert environments. The barren environment requires accurate traversability estimation based on the detected rocks and potholes in the wild navigation. However, the diverse shapes, scales, and occlusions of rocks and potholes make the detection challenging. Current single-sensor approaches (e.g., Camera or LiDAR methods) struggle to obtain accurate locations and landmarks for traversability estimation. However, the light and ground conditions captured by Cameras and the sparsity of LiDAR point cloud pose challenges for singles-sensor pipelines. Additionally, the undulating ground of the desert often makes the sensor calibration with errors, which has a significant influence on the accurate detection. To this end, we present DyFTNet, a LiDAR-Camera fusion-based 3D rock and pothole detection method that specially combines features from both modalities by dynamically correcting point cloud projection errors induced by desert surface irregularities. Subsequently, we construct a multi-modality 3D detection dataset in the desert environment, named U2Ground dataset, consisting of 5534 rocks and 2926 potholes within 1463 pairs of LiDAR-Camera frames. DyFTNet is verified on U2Ground with a comparison to the state-of-the-art methods and superior performance is obtained. The code and dataset will be available on the website https://github.com/Yyb-XJTU/U2Ground-dataset.
在恶劣的沙漠环境中，鲁棒的岩石和坑洼检测对于自主探索至关重要。荒漠环境要求在野外导航中基于检测到的岩石和坑洼进行精确的通行性估计。然而，岩石和坑洼形状多样、尺度不一且存在遮挡，使得检测变得困难。目前单一传感器方法（如相机或激光雷达）难以获得准确的通行位置和地标信息。此外，相机所捕捉的光照和地面条件以及激光雷达点云的稀疏性，给单一传感器的检测流程带来了挑战。同时，沙漠起伏不平的地表常导致传感器校准出现误差，严重影响检测精度。为此，我们提出了一种基于激光雷达与相机融合的3D岩石和坑洼检测方法DyFTNet，该方法通过动态校正由沙漠表面不规则性引起的点云投影误差，专门结合了两种传感器的特征。随后，我们在沙漠环境中构建了一个多模态三维检测数据集，命名为U2Ground数据集，包含1463对LiDAR-相机帧内的5534块岩石和2926个坑洞。DyFTNet在U2Ground上与当前最先进的方法进行对比验证，并取得了优于现有方法的性能表现。代码和数据集将发布在网站 https://github.com/Yyb-XJTU/U2Ground-dataset 上。 |
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