Tag: SLAM

How the Spiking Neural Network allows for navigation through an environment via landmark association without needing GPS?

Alexander Jones, Andrew Rush, Cory Merkel, Eric Herrmann, Ajey P. Jacob, Clare Thiem, Rashmi Jha. A neuromorphic SLAM architecture using gated-memristive synapses. Neurocomputing, 2019, https://doi.org/10.1016/j.neucom.2019.09.098.

Abstract
Navigation in GPS-denied environments is a critical challenge for autonomous mobile platforms

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How Does the Brain Solve the Computational Problems of Spatial Navigation?

Widloski, John, and Ila Fiete. “How does the brain solve the computational problems of spatial navigation?.” In Space, Time and Memory in the Hippocampal Formation, pp. 373-407. Springer, Vienna, 2014.

Abstract
Flexible navigation in the real world involves …

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How to build spatial representation model (x, y, z, yaw) for 3D SLAM inspired by the brain?

The excerpt note is some relevant references about 2.5D SLAM inspired by the brain, which is expanded from the RatSLAM system.

Guth F. A. et. al. present an Hippo 3D (DolphinSLAM), expanded from the RatSLAM system, which was initially designed …

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3D SLAM: the grand challenge and ultimate goal for robotic perception.

Real-world applications in GPS-denied environments require robust mapping and perception techniques to enable mobile systems to autonomously navigate complex 3D environments.

Robotic environments are in general 3D, involving translation in three directions, x, y, and z, and rotation around three …

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How to implement the Loop Closure (Map Correction) in RatSLAM?

This excerpt note is about loop closure (map correction) in RatSLAM from Michael et al., 2008 and Michael 2008 book.

Michael Milford, and Gordon F. Wyeth. “Mapping a Suburb with a Single Camera using a Biologically Inspired SLAM System

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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last …

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基于Occupancy Grids的二维语义制图

基于Occupancy Grids的二维语义制图

2D Semantic Mapping on Occupancy Grids

 

      近年来,学者们对室内矢量地图技术开展了大量研究工作,已经在许多方面得到了很好的应用。SLAM方法能够生成全局一致的矢量地图。尽管这样的地图描述了环境基本的信息并能够支持导航,但仍然缺乏环境的更高层抽象的语义信息或者人们认知的语义信息,例如建筑结构的类别、连通性等。本文中提出了一种新的概率方法基于全覆盖的网格图分析潜在的语义世界模型。该模型是由标准的SLAM方法所产生。文中的方法仿真了一种马尔可夫链从给定输入地图的语义世界模型概率分布产生样本。实验表明该方法是有效的,能够正确捕获到不确定性。

 

      典型的语义概念,比如房间、走廊、空间关系(邻接、连通)、其他属性(如矩形)。这些语义信息有助于构建地图。虽然语义机器人制图没有像矢量或拓扑制图一样得到广泛研究,但也有一些重要的贡献。 文献【11】中的方法作为许多种语义制图方法的先驱,结合了网格和拓扑制图方法能够同时获得高精度、一致性的矢量图和有效拓扑图。 Wolf and Sukhatme [14] 提出了使用隐马尔可夫模型和支持向量机来解决地形制图和基于活动制图中所存在的问题。文献 [10], [2] 和 [3]利用语义标签来标注位置和区域。 Douillard等人提出使用条件随机场(conditional random fields)构建室外环境对象地图。此外,一些学者还提出了利用语义标注环境结构的方法,比如室外环境的道路(traversable terrain)、室内环境中的墙、天花板和门。比较典型的例子如[8], [12], [6] 和[9]。

 

       In recent years, techniques for building metric maps of indoor environments

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