Tolerance-weighted L-optimal experiment design: a new approach to task-directed sensing
Jan De Geeter, Joris De Schutter, Herman Bruyninckx, Hendrik Van Brussel, Marc Decréton · Advanced Robotics · 1998
The choice of 'where to look next' is a special case of an optimal experiment design. This paper proposes the tolerance-optimal experiment design, which is a special instance of the well-known L-optimal design, that minimizes the weighted trace of the covariance matrix of the estimated state under Gaussian assumptions. The weighting matrix is chosen such that the design is invariant to transformations with non-singular Jacobians, and such that the emerging sensing sequence reflects the information needs of the task. This tolerance-optimal design does not require more calculations than existing optimal experiment designs. Existing optimal experiment designs do not reflect the information needs of the task. In addition, some of them physically do not make sense if the estimated state has inconsistent units.