论文标题
使用校准摄像机在2D范围数据中进行自我监督的人检测
Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera
论文作者
论文摘要
深度学习是2D范围数据中最先进的人检测器的重要组成部分。但是，只有少数注释的数据集可用于培训和测试这些深网络，从而在新环境中部署或使用不同的LIDAR模型时可能会限制其性能。我们提出了一种方法，该方法使用校准相机上的基于图像的检测器（例如R-CNN）的边界框来自动生成基于2D激光雷达的人检测器的训练标签（称为伪标记）。通过使用两个检测器模型Drow3和Dr-Spaam在JackRabbot数据集上进行的实验，我们表明，经过pseudo-Labels训练或微调的自我监督检测器，优于仅在其他数据集上训练的探测器。结合强大的训练技术，自我监管的探测器达到了一种使用目标数据集手动注释训练的训练的性能。我们的方法是在部署过程中改善人探测器的有效方法，而无需任何其他标签工作，我们发布了源代码以支持相关的机器人应用程序。
Deep learning is the essential building block of state-of-the-art person detectors in 2D range data. However, only a few annotated datasets are available for training and testing these deep networks, potentially limiting their performance when deployed in new environments or with different LiDAR models. We propose a method, which uses bounding boxes from an image-based detector (e.g. Faster R-CNN) on a calibrated camera to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. Through experiments on the JackRabbot dataset with two detector models, DROW3 and DR-SPAAM, we show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained only on a different dataset. Combined with robust training techniques, the self-supervised detectors reach a performance close to the ones trained using manual annotations of the target dataset. Our method is an effective way to improve person detectors during deployment without any additional labeling effort, and we release our source code to support relevant robotic applications.
