  We consider the problem of sample-based feedback motion planning from
measurements affected by systematic errors. Our previous work presented output
feedback controllers that use measurements from landmarks in the environment to
navigate through a cell-decomposable environment using duality, Control
Lyapunov and Barrier Functions (CLF, CBF), and Linear Programming. In this
paper, we build on this previous work with a novel strategy that allows the use
of measurements affected by systematic errors in perceived depth (similarly to
what might be generated by vision-based sensors), as opposed to accurate
displacement measurements. As a result, our new method has the advantage of
providing more robust performance (with quantitative guarantees) when
inaccurate sensors are used. We test the proposed algorithm in the simulation
to evaluate the performance limits of our approach predicted by our theoretical
derivations.
