In commercial autonomous service robots with several form factors, simultaneous localization and mapping (SLAM) is an essential technology for providing proper services such as cleaning and guidance. Such robots require SLAM algorithms suitable for specific applications and environments. Hence, several SLAM frameworks have been proposed to address various requirements in the past decade. However, we have encountered challenges in implementing recent innovative frameworks when handling service robots with low-end processors and insufficient sensor data, such as low-resolution 2D LiDAR sensors. Specifically, regarding commercial robots, consistent performance in different hardware configurations and environments is more crucial than the performance dedicated to specific sensors or environments. Therefore, we propose a) a multi-stage %hierarchical approach for global pose estimation in embedded systems; b) a graph generation method with zero constraints for synchronized sensors; and c) a robust and memory-efficient method for long-term pose-graph optimization. As verified in in-home and large-scale indoor environments, the proposed method yields consistent global pose estimation for services in commercial fields. Furthermore, the proposed method exhibits potential commercial viability considering the consistent performance verified via mass production and long-term (> 5 years) operation.

翻译：在具有多种形态的商用自主服务机器人中，同步定位与建图（SLAM）是为清洁、引导等任务提供恰当服务的关键技术。此类机器人需要适用于特定应用与环境的SLAM算法。因此，过去十年间已提出了多种SLAM框架以满足不同需求。然而，在处理搭载低端处理器与传感器数据不足（例如低分辨率二维激光雷达）的服务机器人时，我们发现在实施近期创新框架方面面临挑战。具体而言，对于商用机器人而言，在不同硬件配置与环境中的一致性性能，比专用于特定传感器或环境的性能更为关键。为此，我们提出：a) 一种适用于嵌入式系统的多阶段分层全局位姿估计方法；b) 针对同步传感器的零约束图生成方法；以及 c) 一种用于长期位姿图优化的鲁棒且内存高效的方法。在家庭环境与大规模室内环境中的验证表明，所提方法能为商用领域的服务任务提供一致的全局位姿估计。此外，考虑到通过大规模量产与长期（超过5年）运行验证的一致性性能，所提方法展现出潜在的商业可行性。