This paper addresses the pose estimation problem for autonomous vehicles that use a monocular charged-coupled-device (CCD) camera mounted onboard that observes the apparent motion of stationary points. We formulate the problem in the framework of state estimation of a state-affine system with multiple perspective outputs. Resorting to dynamic programming, we derive a minimumenergy estimator which produces an estimate of the state that is “most compatible” with the dynamics, in the sense that it requires the least amount of noise energy to explain the measured outputs. In our formulation we take directly into account that the measurements arrive at discrete-time instants, are time-delayed, and may not be complete. In this way, we can deal with usual problems in vision systems such as noise as well as latency and intermittency of observations. The convergence of the proposed observer system is analyzed and simulations results are presented and discussed.

This paper addresses the state estimation of continuous-time systems with perspective outputs, whose measurements arrive at discrete-time instants, are time-delayed, noisy, and may not be complete. Resorting to dynamic programming, we derive a minimum-energy estimator which produces an estimate of the state that is “most compatible” with the dynamics, in the sense that it requires the least amount of noise energy to explain the measured outputs. The state-estimator has the desired property that, under suitable observability assumptions, the estimate converges asymptotically to the true value of the state in the absence of noise and disturbance. In the presence of noise, the estimate remains bounded away from the true value of the state. We apply these results to the estimation of position and orientation for a mobile robot that uses a monocular chargedcoupled-device (CCD) camera mounted on-board to observe the apparent motion of stationary points. In the context of our application, the estimator can deal directly with the usual problems associated with vision systems such as noise, latency and intermittency of observations. Experimental results are presented and discussed.

This paper addresses the problem of nonlinear filter design to estimate the relative position and attitude of an autonomous vehicle with respect to a desired coordinate system defined by visual landmarks using measurements from an inertial measurement unit (IMU) and a monocular charged-coupled-device (CCD) camera mounted on-board. We formulate the problem in the framework of state estimation of a state-affine system with implicit outputs. Resorting to dynamic programming, we derive a H∞ estimator which produces an estimate of the state that is compatible with the dynamics and ensures a prescribed bound γ on the discounted induced L2-gain from disturbances and noise to estimation error. In our formulation we take directly into account that the measurements arrive at discrete-time instants, are time-delayed, and may not be complete. In this way, we can deal with usual problems in vision systems such as noise as well as latency and intermittency of observations. The convergence of the proposed observer system is analyzed and simulations results are presented and discussed.