As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating a serving robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.

翻译：随着自主移动机器人技术的进步，移动服务机器人已被越来越多地应用于各种场景。特别是自新冠疫情以来，服务机器人已不再令人惊讶。服务机器人在运行中的一个实际问题是，它往往无法在移动的地图上正确估计自身位姿。每当出现这种故障时，服务员需要将服务机器人带回初始位置并手动重启。本文聚焦于服务机器人的端到端重定位以解决这一问题，即通过神经网络直接从机载传感器数据预测机器人位姿。具体而言，我们提出了一种基于相机与二维激光雷达传感器融合的深度神经网络架构用于重定位，并将其命名为FusionLoc。在提出的方法中，多头自注意力机制补充了两种传感器捕获的不同类型信息，以回归机器人位姿。在商用服务机器人采集的数据集上的实验表明，与仅输入单张图像或二维激光雷达点云的现有端到端重定位方法，以及通过拼接特征实现简单融合的方法相比，FusionLoc能够提供更优的性能。