DenseSLAM: Simultaneous Localization and Dense Mapping
DenseSLAM: Simultaneous Localization and Dense Mapping
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DOI：
10.1177/0278364906067379
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发表时间：
2006-08
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通讯作者：
Juan I. Nieto;J. Guivant;E. Nebot
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文献类型：
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作者：
Juan I. Nieto;J. Guivant;E. Nebot
本文解决了同步定位与建图（SLAM）算法的环境表示问题。 SLAM的主要问题之一是如何将外部感知信息解释并合成为移动机器人可以用来自主操作的环境表示。传统上，SLAM 算法依赖于稀疏的环境表示。然而，对于自主导航，需要更详细的环境表示，而经典的基于特征的表示无法为机器人提供足够的信息。虽然密集表示是可取的，但对于 SLAM 范式来说这是不可能的。本文提出了 DenseSLAM，一种获取和维护详细环境表示的算法。该算法在密集的多层地图中表示不同的感官信息。每个图层可以表示环境的不同属性，例如占用率、可遍历性、海拔，或者每个图层可以使用不同的表示来描述相同的环境属性。显示了具有两种不同的稠密图表示的算法的实现。丰富的表示有几个潜在的优势可以帮助导航过程，例如促进使用多维地图的数据关联。本文提出了两个改进本地化过程的特定应用程序；从密集地图中提取复杂地标以及检测具有动态物体的区域。该论文还对使用 DenseSLAM 获得的地图的一致性进行了分析。分析了密集地图中的位置误差，并解释了选择地标以最小化这些误差的方法。该算法使用地面车辆采集的户外实验数据进行了测试。实验结果表明，该算法可以获得密集的环境表示，并且详细的表示可以用于改进车辆定位过程。
This paper addresses the problem of environment representation for Simultaneous Localization and Mapping (SLAM) algorithms. One of the main problems of SLAM is how to interpret and synthesize the external sensory information into a representation of the environment that can be used by the mobile robot to operate autonomously. Traditionally, SLAM algorithms have relied on sparse environment representations. However, for autonomous navigation, a more detailed representation of the environment is necessary, and the classic feature-based representation fails to provide a robot with sufficient information. While a dense representation is desirable, it has not been possible for SLAM paradigms. This paper presents DenseSLAM, an algorithm to obtain and maintain detailed environment representations. The algorithm represents different sensory information in dense multi-layered maps. Each layer can represent different properties of the environment, such as occupancy, traversability, elevation or each layer can describe the same environment property using different representations. Implementations of the algorithm with two different representations for the dense maps are shown. A rich representation has several potential advantages to assist the navigation process, for example to facilitate data association using multi-dimensional maps. This paper presents two particular applications to improve the localization process; the extraction of complex landmarks from the dense maps and the detection of areas with dynamic objects. The paper also presents an analysis of consistency of the maps obtained with DenseSLAM. The position error in the dense maps is analyzed and a method to select the landmarks in order to minimize these errors is explained. The algorithm was tested with outdoor experimental data taken with a ground vehicle. The experimental results show that the algorithm can obtain dense environment representations and that the detailed representation can be used to improve the vehicle localization process.