The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter

problems fit in this framework, including the shortest path, minimum-cost spanning tree, minimum-weight perfect matching, traveling salesman, and Steiner tree problems. Our technique produces approximation algorithms that run in O(n log n) time and come within a factor of 2 of optimal for most

available and potentially profitable innovations actually incorporated in the capital stock.1 In addition, one must introduce an extremely significant empirical hypothesis; namely, that deceleration is the normal optimum path of a sector, due to a variety of factors operating on it, from the side of both

The problems of finding a longest common subsequence of two sequences A and B and a shortest edit script for transforming A into B have long been known to be dual problems. In this paper, they are shown to be equivalent to finding a shortest/longest path in an edit graph. Using this perspective, a

Consider the problem of finding the shortest paths from a node source s to a node sink t in a complete network. On any given instance of the problem, only a subset of the intermediate nodes can be used to go from s to t, the subset being chosen according to a given probability law. We wish to find

. To construct robust and efficient navigation plans, we compute expected shortest paths in such graphs. We formulate the expected shortest paths problem as a Markov decision process and provide two algorithms for its solution. We demonstrate the practicality of our approach using an extensive experimental

robust and efficient navigation plans, we compute “expected shortest paths ” in such graphs. We formulate the expected shortest paths problem as a Markov decision process and provide two algorithms for its solution. We demonstrate the prac-ticality of our approach using an extensive experimental analysis

that arise in dynamic transportation systems, such as intelligent transportation systems (ITS) applications. The all-to-one dynamic shortest paths problem and the one-to-all fastest paths problems are studied. Early results are revisited and new properties are established. The complexity of these problems

). To the best of our knowledge, this is the first parallel implementation of shortest path problems in these metrics. We provide a detailed discussion of the algorithmic issues and the factors related to data, machine, implementation determining the performance of parallel shortest path algorithms. We

Computing the shortest path in a graph is an important problem and it is very useful in various applications. The standard shortest path problem has been studied extensively and intensively, but it can’t handle the situation when there is a switch cost between arcs. For example, in a train