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Graph-based SLAM in indoor environment using corner feature from laser sensor

机译：在室内环境中基于图形的SLAM，使用来自激光传感器的拐角特征

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SLAM (Simultaneous Localization And Mapping) is considered a fundamental problem for robots to become truly autonomous, and it is one of the most popular topic in the field of mobile robotics. When robot works in a unknown environment, it should estimate the current position relative to the environment and meanwhile estimate the environment. When both localization and mapping must be solved concurrently, the problem is called SLAM. SLAM can be implemented in many ways such the Particle Filter, Extended Kalman Filter and Graph-based solution. Currently, one of the most widely used algorithms to solve SLAM is Graph-based solution. In this paper we present a method for robot to calculate its accurate location in indoor environment using graph based optimization. We describe a way how to extract feature from laser range data and how to associate the features, and construct a robot pose graph when robot move in 2D environment. In the last of the paper, we present two simulated robot pose graph to compare the optimization result. The experimental results demonstrate our graph based optimization method is workable.
机译：Slam（同时定位和映射）被认为是机器人真正自主的基本问题，它是移动机器人领域中最受欢迎的主题之一。当机器人在未知环境中工作时，它应该估计相对于环境的当前位置，同时估计环境。当必须同时解决本地化和映射，问题被称为SLAM。 SLAM可以通过这种粒子过滤器，扩展卡尔曼滤波器和基于图的解决方案来实现。目前，使用最广泛使用的算法之一是基于图形的解决方案。在本文中，我们使用基于曲线图的优化来提出一种机器人来计算其在室内环境中的准确位置。我们描述了如何从激光范围数据中提取特征以及如何将功能相关联，并在2D环境中移动机器人时构造机器人姿势图。在本文的最后一篇文章中，我们呈现了两个模拟机器人姿势图以比较优化结果。实验结果表明我们的曲线图的优化方法是可行的。

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