The key of robots operating autonomously in dynamic environments is understanding the dynamic characteristics of objects. This paper aims to detect dynamic objects and reconstruct 3D static maps from consecutive scans of scenes. Our work starts from an encode–decode network, which receives two range maps provided by a Velodyne HDL-64 laser scanner and outputs dynamic probability of each point. Since the soft segmentation produced by the network tends to be smooth, a 3D fully connected CRF (Conditional Random Field) is proposed to improve the segmentation performance. Experiments on both the public datasets and real-word platform demonstrate the effectiveness of our method.

在动态环境中自主操作的机器人的关键是了解对象的动态特性。本文旨在通过连续扫描场景来检测动态对象并重建3D静态地图。我们的工作从一个编码解码网络开始，该网络接收Velodyne HDL-64激光扫描仪提供的两个距离图，并输出每个点的动态概率。由于网络产生的软分段趋于平滑，因此提出了3D全连接CRF（条件随机场）以提高分段性能。在公共数据集和实词平台上的实验证明了我们方法的有效性。