Abstract
In those positioning systems based on the detection of acoustic signals, an accurate detection of the arrival times is crucial for a correct estimation of the distance between nodes, and therefore, for the precise estimation of the node that wants to be located. In order to obtain this arrival time more accurately, acoustic signals can be coded using pseudorandom noise, but these coded signals are still affected by underwater channel phenomena. In this work, the detection of spread-spectrum modulated signals is analyzed in underwater environments that are highly affected by multipath and reverberation. A spread-spectrum signal, which consist of a modulated Kasami code, has been emitted through two different pools, reaching a receiver where it has been captured after following several line-of-sight and non-line-of-sight paths. Then, a correlation process has been performed offline to provide information about the arrival times (times-of-flight) that form the multipath structure. These times-of-flight are compared with those provided by an underwater acoustic propagation model, in order to test the performance of this model and its capacity to predict the outcome of signal detection in underwater environments with a strong multipath and reverberation component. That way, the validated propagation model could be later used in future studies to predict the detection of spread-spectrum signals and the performance of systems that use them in these adverse environments.

Abstract
This paper addresses the problem of bottom following by the MARES Autonomous Underwater Vehicle, and presents derivation of a controller able to cope with the peculiarities of such problem. In specific, the main requirement for the controller is the existence of no overshoot both in the depth and pitch outputs of the system. The existence of such time-domain requirements motivates the use of Eigenstructure Assignment techniques in the formulation of the controller. Simulation results obtained with a dynamic model of the MARES AUV are presented and discussed, indicating the validity of the proposed approach.

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Abstract
This work presents an automatic calibration method for a vision based external underwater ground-truth positioning system. These systems are a relevant tool in benchmarking and assessing the quality of research in underwater robotics applications. A stereo vision system can in suitable environments such as test tanks or in clear water conditions provide accurate position with low cost and flexible operation. In this work we present a two step extrinsic camera parameter calibration procedure in order to reduce the setup time and provide accurate results. The proposed method uses a planar homography decomposition in order to determine the relative camera poses and the determination of vanishing points of detected lines in the image to obtain the global pose of the stereo rig in the reference frame. This method was applied to our external vision based ground-truth at the INESC TEC/Robotics test tank. Results are presented in comparison with an precise calibration performed using points obtained from an accurate 3D LIDAR modelling of the environment.

Abstract
Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.