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 development of vision-based navigation systems for mobile robotics applications in outdoor scenarios is a very challenging problem due to frequent changes in contrast and illumination, image blur, pixel noise, lack of image texture, low image overlap and other effects that lead to ambiguity in the interpretation of motion from image data. To mitigate the problems arising from multiple possible interpretations of the data in outdoor stereo egomotion, we present a fully probabilistic method denoted as probabilistic stereo egomotion transform. Our method is capable of computing 6-degree of freedom motion parameters solely based on probabilistic correspondences without the need to track or commit key point matches between two consecutive frames. The use of probabilistic correspondence methods allows to maintain several match hypothesis for each point, which is an advantage when ambiguous matches occur (which is the rule in image feature correspondence problems), because no commitment is made before analysing all image information. Experimental validation is performed in simulated and real outdoor scenarios in the presence of image noise and image blur. Comparison with other current state-of-the-art visual motion estimation method is also provided. Our method is capable of significant reduction of estimation errors mainly in harsh conditions of noise and blur.

Abstract
This paper presents the work performed in the implementation of an underwater simulation environment for the development of an autonomous underwater vehicle for the exploration of flooded underground tunnels. In particular, the implementation of a laser based structured light system, multibeam sonar and other robot details were addressed. The simulation was used as a relevant tool in order to study and specify the robot multiple sensors characteristics and placement in order to adequately survey a realistic environment. A detailed description of the research and development work is presented along with the analysis of obtained results and the benefits this work brings to the project. © 2017 IEEE.

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
This paper addresses the topic of target detection and tracking using a team of UAVs for maritime border surveillance. We present a novel method on how to integrate the perception into the control loop using two distinct teams of UAVs that are cooperatively tracking the same target. We demonstrate and evaluate the effectiveness of our approach in a simulation environment.