论文标题
DR-SPAAM：2D范围数据中人检测的空间注意事项和自动回归模型
DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data
论文作者
论文摘要
由于2D范围数据的信息内容较低，因此使用2D激光雷达检测人员是一项艰巨的任务。为了减轻激光点的稀疏性引起的问题，当前的最新方法融合了多个先前的扫描，并使用合并的扫描进行检测。这种落后的融合的缺点是，所有扫描都需要明确对齐，并且必要的对齐操作使整个管道更加昂贵 - 通常对于现实世界中的应用程序来说太昂贵了。在本文中，我们提出了一个人检测网络，该网络使用替代策略结合了在不同时间获得的扫描。我们的方法是距离稳健的空间注意力和自动回归模型（DR-SPAAM），遵循前瞻性范式。它可以将骨干网络中的中间功能作为模板保留，并在新扫描可用时将模板重复更新。更新的功能模板依次用于检测当前现场的人。在DROW数据集上，我们的方法优于现有的最新方法，同时大约要快四倍，在带有专用GPU的笔记本电脑上运行速度为87.2 fps，在NVIDIA Jetson Agx嵌入式GPU上运行，在NVIDIA Jetson Agx嵌入式GPU上运行22.6 fps。我们在Pytorch和ROS节点中发布代码，包括预训练的模型。
Detecting persons using a 2D LiDAR is a challenging task due to the low information content of 2D range data. To alleviate the problem caused by the sparsity of the LiDAR points, current state-of-the-art methods fuse multiple previous scans and perform detection using the combined scans. The downside of such a backward looking fusion is that all the scans need to be aligned explicitly, and the necessary alignment operation makes the whole pipeline more expensive -- often too expensive for real-world applications. In this paper, we propose a person detection network which uses an alternative strategy to combine scans obtained at different times. Our method, Distance Robust SPatial Attention and Auto-regressive Model (DR-SPAAM), follows a forward looking paradigm. It keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. The updated feature template is in turn used for detecting persons currently in the scene. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster, running at 87.2 FPS on a laptop with a dedicated GPU and at 22.6 FPS on an NVIDIA Jetson AGX embedded GPU. We release our code in PyTorch and a ROS node including pre-trained models.
