Sentence examples for mapping problem in from inspiring English sources

This paper proposes a new approach, interval Simultaneous Localization and Mapping (i-SLAM), which addresses the robotic mapping problem in the context of interval methods, where the robot sensor noise is assumed bounded.

In this paper, we introduce a multi-objective approach to the zone mapping problem, in which both the total number of clients without QoS and the migration overhead are considered.

From a mathematical viewpoint, the proposed scheme can also be regarded as a numerical implementation of the constructive proof for the existence of a solution of the orthogonal mapping problem in an arbitrary simply-connected domain under the condition that the boundary correspondence is specified on three sides.

Although this response function was already applied to the tone mapping problem in a few literatures [10, 13, 14], most of the previous techniques used this response property as a spatially invariant operator.

The MAQC project had a probe mapping problem in that each gene was detected by a differently designed probe between the different microarray platforms [6].

We demonstrate that Constraint Programming is well-suited to solving the Atom Mapping Problem in this setting.

They proposed a tensorial belief propagation method to solve the maximum a posteriori (MAP) problem in the Markov network.

This method was proposed in [31] for interpolation of scattered data and applied to the indoor radio environment map problem in [7].

However, the many-to-one mapping problem becomes serious in network alignment.

GES successfully solves some constrained discrete optimization TSP-like problems (Mester and Braysy 2005, 2006; http://www.sintef.no/Projectweb/TOP/Problems/VRPTW/Homberger-benchmark/), classic TSP (Mester et al. 2010), and genetic mapping problems formulated in TSP-like terms with the anchor constraints (Mester et al. 2004, 2006, 2010; Korol et al. 2009).

This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem in the stochastic map framework based on the concept of the relative map.