用激光雷达与地图匹配技术，让医院康复机器人更准更稳地自主导航。
Development of a Service Robot for Hospital Environments in Rehabilitation Medicine with LiDAR Based Simultaneous Localization and Mapping
- 采用3D激光雷达与NDT匹配算法实现室内实时定位与建图。
- 在复杂医院环境中导航精度显著提升，实测可靠性高。
- 适合医疗机器人研发者、智能护理系统设计者参考。
本文介绍了配备3D激光雷达和先进定位能力的医疗陪护机器人系统的设计与评估。该机器人基于激光雷达的同步定位与地图构建（SLAM）技术，在复杂动态的医院环境中实现自主导航与有效交互。通过在Linux ROS框架下与Autoware 1.14.0版本的主流3D SLAM技术进行对比分析，验证了系统性能。采用正态分布变换（NDT）匹配算法优化室内导航，实现了高精度实时建图与更强的障碍物规避能力。通过人工操作在多种环境下的测试及ROS仿真模拟挑战场景，验证了系统的响应能力。结果表明，3D激光雷达与NDT匹配的融合显著提升了医院场景下的导航准确性和运行可靠性。研究展示了机器人高效完成医疗辅助任务的能力，并指出了传感器在不同环境条件下的改进空间。该技术的成功部署为支持医护人员、提升患者照护水平提供了可行路径，揭示了未来医疗机器人发展的潜力方向。
原文摘要 · Abstract (English)
This paper presents the development and evaluation of a medical service robot equipped with 3D LiDAR and advanced localization capabilities for use in hospital environments. The robot employs LiDAR-based Simultaneous Localization and Mapping SLAM to navigate autonomously and interact effectively within complex and dynamic healthcare settings. A comparative analysis with established 3D SLAM technology in Autoware version 1.14.0, under a Linux ROS framework, provided a benchmark for evaluating our system performance. The adaptation of Normal Distribution Transform NDT Matching to indoor navigation allowed for precise real-time mapping and enhanced obstacle avoidance capabilities. Empirical validation was conducted through manual maneuvers in various environments, supplemented by ROS simulations to test the system response to simulated challenges. The findings demonstrate that the robot integration of 3D LiDAR and NDT Matching significantly improves navigation accuracy and operational reliability in a healthcare context. This study highlights the robot ability to perform essential tasks with high efficiency and identifies potential areas for further improvement, particularly in sensor performance under diverse environmental conditions. The successful deployment of this technology in a hospital setting illustrates its potential to support medical staff and contribute to patient care, suggesting a promising direction for future research and development in healthcare robotics.
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