论文标题
激光雷达指导小障碍物分段
LiDAR guided Small obstacle Segmentation
论文作者
论文摘要
在道路上发现小障碍对于自动驾驶至关重要。在本文中，我们提出了一种通过稀疏雷达（VLP-16）和单眼视觉的多模式框架可靠地检测出此类障碍的方法。 LIDAR被用来以置信图的形式为单眼分割网络提供其他上下文。当上下文作为单眼语义分割框架的附加输入时，我们会显示出显着的性能。我们进一步向社区介绍了一个新的语义分割数据集，其中包括3000多个图像框架，并带有相应的LIDAR观测值。这些图像带有三个越野，公路和小障碍的三类类别的像素注释。我们强调的是，LiDar和Camera之间的精确校准对于此任务至关重要，因此提出了一种新型的基于Hausdorff距离距离的校准细化方法，而不是外部参数。作为该数据集的第一个基准测试，我们报告了我们的结果，在具有挑战性的情况下，73％的实例检测到50米的距离。通过在50m深度以小于15 cms的障碍的准确分割，并通过对先前的艺术进行定量的比较来定性，我们证实了该方法的功效。我们的项目页面和数据集托管在https://small-obstacle-dataset.github.io/
Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We further present a new semantic segmentation dataset to the community, comprising of over 3000 image frames with corresponding LiDAR observations. The images come with pixel-wise annotations of three classes off-road, road, and small obstacle. We stress that precise calibration between LiDAR and camera is crucial for this task and thus propose a novel Hausdorff distance based calibration refinement method over extrinsic parameters. As a first benchmark over this dataset, we report our results with 73% instance detection up to a distance of 50 meters on challenging scenarios. Qualitatively by showcasing accurate segmentation of obstacles less than 15 cms at 50m depth and quantitatively through favourable comparisons vis a vis prior art, we vindicate the method's efficacy. Our project-page and Dataset is hosted at https://small-obstacle-dataset.github.io/
