基于混合位姿估计模型的移动机器人三维地图创建方法

针对室内复杂环境下的稠密三维建模问题, 提出一种基于RGB-D 相机的移动机器人同时定位与三维地图创建方法. 该方法利用架设在移动机器人上的RGB-D 相机获取环境信息, 根据点云和纹理加权模型建立结合局部纹理约束的混合位姿估计方法, 确保定位精度的同时减小失败率. 在关键帧选取机制下, 结合视觉闭环检测方法, 运用树结构网络优化(TORO) 算法最小化闭环误差, 实现三维地图的全局一致性优化. 在室内环境下的实验结果验证了所提出算法的有效性和可行性.

Mobile robot 3D map building based on hybrid pose estimation model

A real-time dense method to address the problem of mobile robot simultaneous localization and 3D mapping(3D SLAM) in complex indoor environment is proposed. In this approach, the environmental data is captured by using a RGB-D camera which is fixed on the robot. Combining with the local texture association, a hybrid algorithm model is established to ensure the pose estimation accuracy and concurrently decrease the failure rate during mapping by using the point cloud and image texture. By taking advantage of the keyframe selection mechanism, a visual-based loop detection algorithm and tree-based network optimizer(TORO) are used to achieve a global consistency map. Experimental results show the feasibility and effectiveness of the proposed algorithm in the indoor environment.