Abstract
This paper addresses a three-dimensional (3D) reconstruction of a flooded open pit mine with an autonomous surface vehicle (ASV) and unmanned aerial vehicle (UAV). The ROAZ USV and the Otus UAV were used to provide the underwater bathymetric map and aerial 3D reconstruction based from image data. This work was performed wihtin the context of the European researcj project VAMOS with the objective of developing robotic tools for efficient underwater mining

Abstract
A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. Themethod aims to identify shipwrecked people by recognizing the hand waving gesture sign. We introduce a novelmotion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously. The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

Abstract
This paper describes the calibration of an underwater navigation system in enclosed scenarios. The work was performed in the context of the VAMOS project addressing the development of robotic solutions for flooded open pit mine exploration. An algorithm for calibration of extrinsic parameters for DVL and USBL systems is presented. Field experiments were performed with the ROAZ autonomous surface vehicle equipped with the underwater sensors and using precision IMU/GNSS fused data as groundtruth. The tests were performed in Douro River and in the Bejanca open pit mine, one of the VAMOS test sites, both in northern Portugal. The procedure was validated in the operational scenarios and results are presented showing the error correction and navigation quality improvement.

Abstract
The planning of mining operations in water filled open-pit mines requires detailed bathymetry to create a mine plan and assess the involved risks. This paper presents post processing techniques for creating an improved 3D model from a survey carried out using an autonomous surface vehicle with a multibeam sonar and a GPS/INS navigation system. Inconsistencies of the created point cloud as a result of calibration errors or GPS signal loss are corrected using a continuous-time simultaneous localization and mapping (SLAM) solution. Signed distance function (SDF) based mapping is employed to fuse the measurements from multiple runs into a consistent representation and reduce sensor noise. From the signed distance function model we reconstruct a 3D surface mesh. We use this terrain model to establish a virtual reality scene for immersive data visualization of the mining operations for testing and planing during development. Results of the proposed approach are demonstrated on a dataset captured in an abandoned submerged inland mine.

Abstract
Limited perception capabilities underwater shrink the envelope of effective localization techniques that can be applied in this environment. Long-term localization in six degrees of freedom can only be achieved by combining different sources of information. A multiple vehicle underwater localization solution, for localizing an underwater mining vehicle and its support vessel, is presented in this paper. The surface vessel carries a short baseline network, that interact with the inverted ultra-short baseline, carried by the underwater mining vehicle. A multiple antenna GNSS system provides data for localizing the surface vessel and to georeference the short baseline array. Localization of the mining vehicle results from a data fusion approach, that combines multiple sources of sensor information using the Extended Kalman Filter (EKF) framework. The developed solutions were applied in the context of the VAMOS! European project. Long-term real time position errors below 0.2 meters, for the underwater machine, and 0.02 meters, for the surface vessel, were accomplished in the field. All presented results are based on data acquired in a real scenario.

Abstract
This paper presents the positioning, navigation and awareness (PNA) system developed for the Underwater Robotic Mining System of the VAMOS! project [1]. It describes the main components of the VAMOS! system, the PNA sensors in each of those components, the global architecture of the PNA system, and its main subsystems: Position and Navigation, Real-time Mine Modeling, 3D Virtual reality HMI and Real-time grade system. General results and lessons learn during the first mining field trial in Lee Moor, Devon, UK during the months of September and October 2017 are presented.