This paper presents an experimentally evaluated solution to the problem of estimating the attitude of a rigid body using rate gyros and a pan-tilt camera. A nonlinear attitude observer combines angular velocity measurements obtained from rate gyros with images of a planar scene provided by the camera. By directly exploiting the sensor information, a stabilizing feedback law is introduced, and exponential convergence to the origin of the estimation errors is shown. Additionally, an active-vision system is proposed that relies on an image-based exponentially input-to-state-stable control law for the pan and tilt angular rates of the camera to keep the features in the image plane. Using recent results in geometric numerical integration, a multirate implementation of the observer is proposed, which exploits the complementary bandwidth of the sensors. Practical considerations, such as the lens-distortion compensation and the computation of suitable observer feedback gains, are considered. Experimental results obtained with a high-accuracy motion rate table demonstrate the high level of performance attained by the proposed solution.

This paper proposes two estimation algorithms for the determination of attitude installation matrix for Laser Detection and Ranging systems (LADAR) mounted onboard autonomous vehicles. The use of autonomous vehicles equipped with LADAR systems to conduct fully automatic surveys of terrain, infrastructures, or just to navigate safely in unknown environments, motivates the research on increasingly precise LADAR data acquisition and processing algorithms, for which the determination of the correct installation matrix is critical. The proposed methods rely on the minimization of the errors between the measured data set and a representation of the real calibration surface. To minimize this error, two nonlinear optimization techniques are proposed, one that estimates the ZYX Euler angles and a second that uses optimization tools for Riemannian manifolds enabling direct estimation of the installation matrix on the group of special orthogonal matrices SO(3). The proposed techniques are extensively tested and their effectiveness compared resorting to simulated LADAR data sets under realistic noise conditions.

This paper presents a new ultra-short baseline (USBL) tightly-coupled integration technique to enhance error estimation in low-cost strap-down inertial navigation systems (INSs) with application to underwater vehicles. In the proposed strategy the acoustic array spatial information is directly exploited resorting to the extended Kalman filter implemented in a direct feedback structure. The determination and stochastic characterization of the round trip travel time are obtained resorting to pulse detection matched filters of acoustic signals modulated using spread-spectrum code division multiple access (CDMA). The performance of the overall navigation system is assessed in simulation and compared with a conventional loosely-coupled solution that consists of solving separately the triangulation and sensor fusion problems. From the simulation results it can be concluded that the proposed technique enhances the position, orientation, and sensors biases estimates accuracy