Abstract
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.
Highlights
A mobile robot operating in a dynamic environment is provided with sensors in order to perceive its environment
For a mobile robot, the automatic recognition of features is an important issue for the following reasons: 1. For successful navigation in large-scale environments, mobile robots must have the capability to localize themselves in their environment
At the topological level of his “spatial semantic hierarchy” system, Kuipers [15] incrementally builds a topological map by first detecting pertinent features while the robot moves in the environment and determining the link between a new detected feature and features contained in the current map; 3
Summary
A mobile robot operating in a dynamic environment is provided with sensors (infrared sensors, ultrasonic sensors, tactile sensors, cameras. . . ) in order to perceive its environment. We build models that represent the statistical properties of the data This approach naturally takes into account the noisy data, but it is generally difficult to understand the correspondence between detected features and the sensor data. Consider for example the detection of a feature in [13] or the construction of an evidence grid in [25]: these two operations use a temporal sequence of sensor information. A very complete tutorial on first order Hidden Markov Models can be found in Rabiner [19]
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