Underwater Robotics Research Center (CIRS), Computer Vision and Robotics Institute (VICOROB), Universitat de Girona, 17004 Girona, Spain.
Sensors (Basel). 2018 May 1;18(5):1386. doi: 10.3390/s18051386.
Occupancy Grid maps provide a probabilistic representation of space which is important for a variety of robotic applications like path planning and autonomous manipulation. In this paper, a SLAM (Simultaneous Localization and Mapping) framework capable of obtaining this representation online is presented. The H-SLAM (Hilbert Maps SLAM) is based on Hilbert Map representation and uses a Particle Filter to represent the robot state. Hilbert Maps offer a continuous probabilistic representation with a small memory footprint. We present a series of experimental results carried both in simulation and with real AUVs (Autonomous Underwater Vehicles). These results demonstrate that our approach is able to represent the environment more consistently while capable of running online.
占据栅格地图为空间提供了一种概率表示，这对于各种机器人应用非常重要，例如路径规划和自主操作。在本文中，提出了一种能够在线获得这种表示的同时定位与地图构建（SLAM）框架。H-SLAM（希尔伯特地图 SLAM）基于希尔伯特地图表示，并使用粒子滤波器来表示机器人状态。希尔伯特地图提供了具有小内存占用的连续概率表示。我们展示了一系列在模拟和真实 AUV（自治水下车辆）中进行的实验结果。这些结果表明，我们的方法能够更一致地表示环境，同时能够在线运行。
