With the recent development of autonomous driving technology, as the pursuit of efficiency for repetitive tasks and the value of non-face-to-face services increase, mobile service robots such as delivery robots and serving robots attract attention, and their demands are increasing day by day. However, when something goes wrong, most commercial serving robots need to return to their starting position and orientation to operate normally again. 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. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous 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：随着自动驾驶技术的近期发展，以及对于重复性任务效率的追求与非面对面服务价值的提升，诸如配送机器人和服务机器人等移动服务机器人受到关注，其需求与日俱增。然而，当出现故障时，大多数商用服务机器人需要返回其起始位置和方向才能恢复正常运行。本文聚焦服务机器人的端到端重定位问题，旨在利用神经网络直接从车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-2D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。在该方法中，多头自注意力机制能够互补两种传感器捕获的不同类型信息。我们在商用服务机器人采集的数据集上进行的实验表明，FusionLoc相比仅利用单目图像或2D激光雷达点云的传统重定位方法，以及简单的特征拼接融合方法，均能提供更优的性能。