— For the Simultaneous Localization and Mapping problem several efficient algorithms have been proposed that make use of a sparse information matrix representation (e.g. SEIF, T...
This paper aims at a discussion of the structure of the SLAM problem. The analysis is not strictly formal but based both on informal studies and mathematical derivation. The first ...
In this paper we describe an automatic procedure for successively reducing the set of possible nonzeros in a Jacobian matrix until eventually the exact sparsity pattern is obtained...
Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM sc...
Compressive sensing predicts that sufficiently sparse vectors can be recovered from highly incomplete information. Efficient recovery methods such as 1-minimization find the sparse...