Abstract
Autonomous Surface Vehicles (ASVs), operating near ship harbors or relatively close to shorelines must be able to steer away from incoming vessels and other possible obstacles, be they dynamic or not. To do this, one must implement some type of multi-target tracking and obstacle avoidance algorithms that lets the vehicle dodge obstacles. This paper presents a radar-based multi-target tracking system developed for obstacle detection in a small unmanned surface vehicle. The system was designed for ROAZ II ASV belonging to INESC TEC/ISEP and implemented in Robot Operating System (ROS) for easier integration with the already existing software.

Abstract

Abstract
The Braçal –Malhada mining region, 30km NE of Aveiro included several silver-lead mines that operated until mid 20th century. Mineralization consists on sulphides (galena, sphalerite, pyrite) in quartz veins occurring in the complex Porto-Tomar shear zone, Fig. 1. Veins develop in a 10km wide area, show different orientations, are found in granites, schists and even quartzite, and can pass through different rock types with no alteration. Exploration work in the area dates from the 1950´s and 70’s. This study compiles recent geological mapping, preliminary regional geochemical and geophysical surveys. It aims to obtain a general information on the area, delineate regions for further studies, that is, fieldwork with closer grids, complementary methods (such as electromagnetics, induced polarization), etc. Because of space limitations, only selected maps are given herein. Geophysics consisted on preliminary magnetic and gravimetric mapping. Residual, gradient, second derivative and Euler deconvolution magnetic and gravimetric maps were produced, interpreted against known geology, tectonics and interesting features for further exploration work are revealed. A stream sediments sampling geochemical campaign, overall 101 samples, was carried out and allowed identifying the main geochemical association of elements. Finally, geophysical and geochemical data are integrated to provide an enhanced view of the area.

Abstract
The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this paper, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior(DCP) and then taking the converted images and modifying them into the Long, Medium and Short(LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at INESC TEC robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. The paper describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation.

Abstract
Unmanned Aerial Vehicles (UAVs) are a key ingredient in the industry and in warehouse logistics digital transformation process, providing the ability to perform automatic cyclic counting and real-time inventory, localize hard-to-find items and reach narrow storage areas. The use of UAVs poses new challenges, such as indoor autonomous localization and navigation, collision avoidance and automated UAV fleet management. This paper addresses the development of a vision-based Graph-SLAM approach for UAV indoor localization without predefined warehouse markers positions. A framework is proposed and developed to support different commercial UAV platforms, allowing the estimation in real-time of the UAV position and attitude. Indoor experimental tests were carried out in order to evaluate the performance of the developed method, comparing the results obtained with an approach based on the pre-mapped markers position indoor localization method.

Abstract
This work presents a six degrees of freedom probabilistic scan matching method for registration of 3D underwater sonar scans. Unlike previous works, where local submaps are built to overcome measurement sparsity, our solution develops scan matching directly from the raw sonar data. Our method, based on the probabilistic Iterative Correspondence (pIC), takes measurement uncertainty into consideration while developing the registration procedure. A new probabilistic sensor model was developed to compute the uncertainty of each scan measurement individually. Initial displacement guesses are obtained from a probabilistic dead reckoning approach, also detailed in this document. Experiments, based on real data, demonstrate superior robustness and accuracy of our method with respect to the popular ICP algorithm. An improved trajectory is obtained by integration of scan matching updates in the localization data fusion algorithm, resulting in a substantial reduction of the original dead reckoning drift.