Sun Ruo-Huai, Zhao Xue, Wu Cheng-Dong, Zhang Lei, Zhao Bin
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China.
Sensors (Basel). 2024 Jul 4;24(13):4341. doi: 10.3390/s24134341.
This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.
本文提出了一种针对大场景动态环境中移动机器人导航与轨迹插值问题的解决方案。该解决方案结合了利用深度学习的语义激光同步定位与地图构建（SLAM）系统和轨迹插值算法。本文首先介绍了一些开源激光SLAM算法，然后详细阐述了本文所使用的SLAM系统的总体框架。其次，将体素概念引入占用概率地图，以增强局部体素地图表示动态物体的能力。然后，本文提出了一种结合深度学习算法的PointNet++点云语义分割网络，用于提取大场景中动态点云的深度特征，并输出静态物体上点的语义信息。基于其语义信息生成全局环境描述符。进行全局地图优化的闭环完成以减少累积误差。最后，利用T轨迹插值来确保机器人的运动性能并提高机器人轨迹的平滑稳定性。实验结果表明，本文提出的深度学习语义激光SLAM系统与轨迹插值算法相结合，在新松大场景校园的大场景中产生了更好的建图和闭环效果。使用T轨迹插值可确保目标点之间无振动且平稳过渡。
