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
This paper presents the autononomous aerial vehicle OTUS and its application to search and rescue scenarios, namely the participation on the EuRathlon 2015 competition. The OTUS robot was developed at INESC TEC/ ISEP for research in cooperative aerial robotics and applications in complex and dynamic environments. The system was validated in this challenging scenario and was able to win the Grand Challenge scenario in cooperation with a land and marine robotics partner teams.

Abstract
It is commonly accepted that one of the most important factors for assuring the high performance of an electrical network is the surveillance and the respective preventive maintenance. From a long time ago that TSOs and DSOs incorporate in their maintenance plans the surveillance of the grid, where is included the aerial power lines inspection. Those inspections started by human patrol, including structure climbing when needed and later were substituted by helicopters with powerful sensors and specialised technicians. More recently the Unmanned Aerial Vehicles (UAV) technology has been used, taking advantage of its numerous advantages. This paper addresses the problem of improving the real-time perception capabilities of UAVs for endowing them with capabilities for safe and robust autonomous and semi-autonomous operations. It presents a new vision based power line detection algorithm denoted by PLineD, able to improve the detection robustness even in the presence of image with background noise. The algorithm is tested in real outdoor images of a dataset with multiple backgrounds and weather conditions. The experimental results demonstrate that the proposed approach is effective and able to implemented in real-time image processing pipeline.

Abstract
The multirotor UAVs are being integrated into a wide range of application scenarios due to maneuverability in 3D, versatility and reasonable payload of sensors. One of the application scenarios is the inspection of structures where the human intervention is difficult or unsafe and the UAV can provide an improvement of the collected data. At the same time introduce challenges due to low altitude missions and also the fact of being manually operated without line of sight. In order to overcome these issues, this paper presents a LiDAR-based realtime collision avoidance algorithm, denoted by Escape Elliptical Search Point with the ability to be integrated into autonomous and manned modes of operation. The algorithm was validated in a simulation environment developed in Gazebo and also in a mixed environment composed by a real robot in an outdoor scenario and simulated obstacle and LiDAR.

Abstract
This paper presents the results from search and rescue missions performed with the aerial robot OTUS in the the context of the ISEP/INESC TEC aerial robotics team participation on the euRathlon 2015 robotics competition. The multi-domain (land, sea and air) search and rescue scenario is described and technical solution adopted is presented with emphasis on the perception system. The calibration of the image based system is addressed. Results from the operational missions performed are also discussed. The aerial autonomous vehicle was able to successfully perform multiple tasks from the aerial reconnaissance and 3D mapping to the identification of leaking pipes, obstructed passages and missing workers. The system was validated a realistic operational scenario and won the Grand Challenge in cooperation with land and marine robotics partner teams. This challenge was the first time that a real time collaborative team of aerial, land and marine robots was deployed successfully in a search and rescue mission.

Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL2DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.