  An optimization method is proposed in this paper for novel deployment of
given number of directional landmarks (location and pose) within a given region
in the 3-D task space. This new deployment technique is built on the geometric
models of both landmarks and the monocular camera. In particular, a new concept
of Multiple Coverage Probability (MCP) is defined to characterize the
probability of at least n landmarks being covered simultaneously by a camera at
a fixed position. The optimization is conducted with respect to the position
and pose of the given number of landmarks to maximize MCP through globally
exploration of the given 3-D space. By adopting the elimination genetic
algorithm, the global optimal solutions can be obtained, which are then applied
to improve the convergent performance of the visual observer on SE(3) as a
demonstration example. Both simulation and experimental results are presented
to validate the effectiveness of the proposed landmark deployment optimization
method.
