基于二阶隐马尔可夫模型的移动机器人特征自动检测
Learning to automatically detect features for mobile robots using second-order Hidden Markov Models
摘要：
在本文中，我们提出了一种新的方法，基于隐马尔可夫模型来解释时间序列的传感器数据从移动机器人自动检测功能。隐马尔可夫模型在模式识别，特别是语音识别中的应用由来已久。与其他方法（如神经网络）相比，它们的主要优点是能够对可变长度的噪声时间信号进行建模。在本文中，我们表明，这种方法是非常适合的时间序列的移动机器人传感器数据的解释。我们提出了两个不同的实验和结果：第一个在室内环境中，移动机器人学习检测功能，如打开的门或T形路口，第二个在室外环境中，不同的移动机器人必须识别的情况下，如爬山或穿越岩石。
英文摘要：
In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
