艾青林, 刘刚江, 徐巧宁. 动态环境下基于改进几何与运动约束的机器人RGB-D SLAM算法[J]. 机器人, 2021, 43(2): 167-176. DOI: 10.13973/j.cnki.robot.200147
引用本文: 艾青林, 刘刚江, 徐巧宁. 动态环境下基于改进几何与运动约束的机器人RGB-D SLAM算法[J]. 机器人, 2021, 43(2): 167-176. DOI: 10.13973/j.cnki.robot.200147
AI Qinglin, LIU Gangjiang, XU Qiaoning. An RGB-D SLAM Algorithm for Robot Based on the Improved Geometric andMotion Constraints in Dynamic Environment[J]. ROBOT, 2021, 43(2): 167-176. DOI: 10.13973/j.cnki.robot.200147
Citation: AI Qinglin, LIU Gangjiang, XU Qiaoning. An RGB-D SLAM Algorithm for Robot Based on the Improved Geometric andMotion Constraints in Dynamic Environment[J]. ROBOT, 2021, 43(2): 167-176. DOI: 10.13973/j.cnki.robot.200147

动态环境下基于改进几何与运动约束的机器人RGB-D SLAM算法

An RGB-D SLAM Algorithm for Robot Based on the Improved Geometric andMotion Constraints in Dynamic Environment

  • 摘要: 室内移动机器人使用传统视觉SLAM算法在动态场景下进行位姿估计时精度低、鲁棒性差,其主要原因是错误地将运动的特征点加入了相机位姿计算.为了解决这一问题,本文将特征点分为静态特征点、状态未知点、可疑静态特征点、动态特征点和错误匹配点.其中,静态特征点使用严格的几何约束进行筛选,并将状态未知点使用多帧的观测信息区分为可疑静态特征点、动态特征点以及错误匹配点,并进行卡尔曼滤波.最后,将静态特征点、可疑静态特征点和动态特征点一起加入位姿优化.利用TUM数据集,在室内存在运动物体的场景下进行实验.结果表明,所提出的算法在动态场景下的综合性能(包括精度、鲁棒性、运行速度)要优于其他动态场景下的SLAM算法.

     

    Abstract: The pose estimation accuracy and robustness of the indoor robot using traditional visual SLAM (simultaneous localization and mapping) systems in dynamic environments are poor because the moving feature points are incorrectly incorporated into the camera pose calculation. In order to solve this problem, the feature points are divided into 5 categories, including static feature points, unknown feature points, suspected static feature points, dynamic feature points, and mismatched feature points. Static feature points are screened with strict geometric constraints. Unknown feature points are divided into suspected static feature points, dynamic feature points, and mismatched feature points using multi-frame observation information, and Kalman filtering is performed. Finally, the static feature points, suspected static feature points, and dynamic feature points are all used for pose optimization. The experiments on the TUM dataset in indoor environments with moving objects show that the overall performance (including accuracy, robustness, and running speed) of the proposed algorithm is superior to other dynamic SLAM algorithms.

     

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