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首页> 外文期刊>Engineering Computations >Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks
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Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks

机译：使用人造地标在动态和混乱环境中进行室内移动机器人定位

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Purpose Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot's environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot's pose in extremely dynamic, cluttered indoors environments. Design/methodology/approach The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot's pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot's pose in extremely dynamic, cluttered indoors environment. Findings The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima. Originality/value By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.
机译：目的在动态，混乱的环境中进行机器人定位是一个具有挑战性的问题，因为要拥有足够的知识以这种方式准确地对机器人环境进行建模是不切实际的。这项研究旨在开发一种配备函数逼近技术的新概率方法，该方法能够通过利用最大统计能力来适当地对Markov定位中的数据分布进行建模，从而在极其动态，混乱的室内环境中对机器人的姿势进行合理准确的估计。设计/方法/方法统计模型的参数向量采用易检测的人工界标在全向图像中的位置形式。首先，使用概率主成分分析，从包含缺失值的传感器数据集中提取环境模型的最可能参数集。接下来，使用支持向量回归，使用这些参数来近似概率密度函数，该支持向量回归能够在马尔可夫定位的每种状态下计算机器人的姿态向量。最后，使用此密度函数，可以很好地逼近与观察模型相关的条件密度，从而可以在非常动态，杂乱的室内环境中对机器人的姿势进行合理准确的估计。结论作者在具有34个独特人工地标的室内办公环境中验证了他们的方法。此外，它们表明，即使精度显着提高了环境的动态性，精度仍然很高。他们还表明，与那些依赖于图像像素的基于外观的定位方法相比，所提出的定位策略在准确性和收敛到全局极小值的速度方面更为出色。独创性/价值通过使用易于检测且旋转，尺度不变的人工地标以及通过缺失数据的概念提供的最大统计能力，作者成功确定了精确的姿态更新，而无需太多的计算资源来分析全向图像。另外，所提出的方法通过消除具有相似状态的可能性，大大降低了陷入局部最小值的风险。

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