基于多维度视觉约束的床椅机器人SLAM  

A SLAM Method for Bed-and-chair Robot Based on Multi-dimensional Visual Constraints

作　　者：李秀智[1,2] 邓向军 韩红桂[1,2] 杜博文 LI Xiuzhi;DENG Xiangjun;HAN Honggui;DU Bowen(School of Information Science and Technology,Beijing University of Technology,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China)

机构地区：[1]北京工业大学信息科学技术学院,北京100124 [2]数字社区教育部工程研究中心,北京100124

出　　处：《机器人》2025年第1期44-54,共11页Robot

基　　金：国家自然科学基金(U22A2049)。

摘　　要：为了应对楼宇环境中的低纹理和动态场景给机器人定位带来的巨大挑战,提出了一种面向楼宇结构化场景的多源数据融合定位方法。通过合理引入多基元视觉特征、曼哈顿世界假设约束以及惯性测量单元(IMU)预积分约束,本文定位系统能有效克服智能床椅机器人在工作时遇到的特征稀疏、动态人员干扰等问题。本文总体采用高频前端位姿估计和低频后端优化的并行策略来提高系统实时性。前端里程计在短时间内提供相对准确的位姿估计,而后端优化则利用曼哈顿假设对位姿估计进行进一步优化。同时针对曼哈顿世界假设在位姿估计应用中的局限性,创新性地提出使用多相机联合定位的策略。利用两个不同朝向的相机,分别捕捉地面丰富的纹理特征以及满足曼哈顿世界假设的上层空间中的平面,有效排除了非曼哈顿世界假设物体对定位的影响。实验结果表明,与现有方法相比,本方法在保证实时性的同时,显著提高了定位准确性和鲁棒性,为智能移动床椅机器人在楼宇环境中的应用提供了有力的支持。To address the significant challenges posed by low-texture and dynamic scenes in building environments for robotic localization,a multi-source data fusion based localization method tailored for structured building scenarios is proposed.By judiciously incorporating multi-primitive visual features,Manhattan World Hypothesis constraints,and IMU(inertial measurement unit)pre-integration constraints,the proposed localization system effectively tackles issues such as sparse features and dynamic personnel interference encountered by intelligent bed-and-chair robots during operation.Overall,a parallel strategy combining high-frequency front-end pose estimation and low-frequency backend optimization is adopted to enhance system real-time performance.The front-end odometry provides relatively accurate pose estimates within short periods,which are further refined by backend optimization using the Manhattan World Hypothesis.Additionally,a multi-camera joint localization strategy is introduced innovatively to address the limitations of the ManhattanWorld Hypothesis in pose estimation applications.Two cameras with different orientations are utilized,one to capture rich texture features on the ground and the other to identify planes in the upper vertical space that satisfy the Manhattan World Hypothesis,which mitigates the impact of non-Manhattan objects on localization.Experimental results demonstrate that,compared to the existing methods,the proposed method significantly improves the localization accuracy and robustness while ensuring real-time performance,providing robust support for the application of intelligent mobile bed-and-chair robots in building environments.

关 键 词：智能床椅机器人 同步定位与地图创建 曼哈顿世界 多特征基元 多源数据融合 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]