Parameterized Sensor Model and an Approach for Measuring Goodness of Robotic Maps

Y. Arafat (USA), T. Hellström, and J. Rashid (Sweden)


Robotics, Intelligent Robotics, Sensor Model, RoboticMap, Map Goodness.


Map building is a classical problem in mobile and au tonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and evaluated. Three different techniques; statistical anal ysis, derivative of images, and comparison of binary maps have been used as estimates of map goodness. The results show that the proposed sensor model generates better maps than a standard sensor model. However, the proposed ap proach of measuring goodness of maps does not improve the results as much as expected.

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