LIDAR-based SLAM implementation using Kalman filter
LIDAR-based SLAM implementation using Kalman filter
复制标题
使用卡尔曼滤波器实现基于 LIDAR 的 SLAM
DOI：
10.1117/12.2564818
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
P. Kaniewski
中科院分区：
文献类型：
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作者：
Pawel Slowak;P. Kaniewski
准确导航的能力是移动机器人应该能够自主执行任务的特征之一。在 GPS/GNSS 无法使用的环境中，例如建筑物内，移动平台的定位是一个特别具有挑战性的问题。在这种情况下，为了让机器人能够确定其位置并分析其周围环境，可以实施同步定位和建图（SLAM）算法。在本文中，我们提出了一个 SLAM 系统，该系统使用卡尔曼滤波器以及 2D LiDAR 收集的数据。我们的方法应用 ICP 算法来计算定位，并采用聚类和形状识别技术来构建环境地图。本文详细描述了所提出的 SLAM 解决方案的各个元素。此外，它还提供了验证系统的实验结果。
The capability to navigate accurately is one of the features, that a mobile robot should have to be able to perform tasks autonomously. In a GPS/GNSS-denied environment, for example inside buildings, localization of a mobile platform is an especially challenging problem. In such cases, to provide a robot with the ability to determine its position and to analyze its surroundings, Simultaneous Localization and Mapping (SLAM) algorithms could be implemented. In the article, we present a SLAM system that uses a Kalman filter together with data gathered by a 2D LiDAR. Our approach applies the ICP algorithm to calculate the localization and employs clustering and shape recognition technics to build the map of the environment. The article contains a detailed description of the individual elements of the proposed SLAM solution. Furthermore, it presents the results of experiments during which the system was validated.