论文标题
GP-SLAM+：基于改进的区域化高斯过程图的实时3D LIDAR SLAM重建
GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction
论文作者
论文摘要
本文基于改进的区域化高斯流程（GP）地图重建，提出了一个3D激光雷达大满贯系统，以实时为机器人应用程序提供低饮料状态估计和实时映射。我们利用空间GP回归来对环境进行建模。该工具使我们能够恢复包括稀疏扫描区域的表面，并获得不确定性的统一样品。这些属性促进了在我们的扫描到地图注册方案中的强大数据关联和地图更新，尤其是在使用稀疏范围数据时。与以前的GP-SLAM相比，这项工作克服了GP的过度计算复杂性，并重新设计了注册策略，以满足3D情况下的准确性要求。对于大规模的任务，采用了一个两线程框架来进一步抑制漂移。天线和地面实验表明，我们的方法允许实时稳健的探光仪和精确的映射。它还胜过使用轻型传感器的测试中最先进的激光雷达大满贯系统。
This paper presents a 3D lidar SLAM system based on improved regionalized Gaussian process (GP) map reconstruction to provide both low-drift state estimation and mapping in real-time for robotics applications. We utilize spatial GP regression to model the environment. This tool enables us to recover surfaces including those in sparsely scanned areas and obtain uniform samples with uncertainty. Those properties facilitate robust data association and map updating in our scan-to-map registration scheme, especially when working with sparse range data. Compared with previous GP-SLAM, this work overcomes the prohibitive computational complexity of GP and redesigns the registration strategy to meet the accuracy requirements in 3D scenarios. For large-scale tasks, a two-thread framework is employed to suppress the drift further. Aerial and ground-based experiments demonstrate that our method allows robust odometry and precise mapping in real-time. It also outperforms the state-of-the-art lidar SLAM systems in our tests with light-weight sensors.
