  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.
