Vlassis, N.A., Tsanakas. 1998. A Sensory Uncertainty Field Model for Unknown and Non-stationary Mobile Robot Environments. In Proceedings of the 1998 IEEE International Conference on Robotics & Automation .  Home/Search Document Details and Download
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Automatic Mapping of Dynamic Office Environments - Kunz, Willeke, Nourbakhsh (1997) (7 citations) (Correct)
....subtle case would be a populated building, with peoples, boxes and chairs on the move. Geometric automatic mapping systems are generally capable only of monotonically creating their maps of the world (Thrun et al. 1998) Castellanos et al. 1997) Delahoche et al. 1998) Araujo et al. 1998) (Vlassis Tsanakas 1998). Two notable exceptions are (Murray Jennings 1997) and (Yamauchi et al. 1998) Murray Jennings 1997) have developed a threecamera ranging system that serves as the sole ranging sensor of a robot that automatically maps an office environment. Their architecture has a reliance on a static ....
....structure in parts of the world that are still unseen, and then explore those 10 structures. This is consonant with the approach taken in (Yamauchi et al. 1998) and stands in contrast to methods in which the mobile robot does not explicitly plan and act for the sake of information gain (Vlassis Tsanakas 1998); Araujo et al. 1998) Delahoche et al. 1998) Thrun et al. 1998) This additional step is crucial in enabling InductoBeast to deal with dynamic worlds and with obstacles. Since the present system looks at the motion behavior of the robot over the long term, any transient sensor readings due ....
Vlassis, N.A., Tsanakas. 1998. A Sensory Uncertainty Field Model for Unknown and Non-stationary Mobile Robot Environments. In Proceedings of the 1998 IEEE International Conference on Robotics & Automation .
Dynamic Sensory Probabilistic Maps for Mobile Robot.. - Vlassis.. (1998) Self-citation (Vlassis Tsanakas) (Correct)
....the sensor densities at the respective configurations. We propose a combined algorithm for map update and robot localization. 1 Introduction Recently, there has been an increasing interest in the mobile robots community in probabilistic models for robot localization and navigation in metric maps [7, 1, 4, 11, 8]. The key issue in most of these approaches is a probabilistic map, i.e. an assignment to each robot s configuration q of a probability density function that models the uncertainty of the sensor readings when observing an environment landmark from q. At any instant, the robot combines the ....
....density estimation is performed only at specific landmarks of the environment and an on line procedure is proposed which combines map generation with on line robot localization based on the BaumWelch method for Maximum Likelihood estimation of the parameters of a hidden Markov model. Finally, in [4, 11] neural network models based on probabilistic maps are proposed for position estimation and path planning. In this paper we propose a method for building and maintaining sensory probabilistic maps based on proximity sensor information that can be changing with time. As in [1] we assume a ....
N. A. Vlassis and P. Tsanakas. A sensory uncertainty field model for unknown and nonstationary mobile robot environments. In Proc. ICRA'98, IEEE Int. Conf. on Robotics and Automation, Leuven, Belgium, May 1998.
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