A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping.

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

Monocular Visual SLAM Based on a Cooperative UAV-Target System.

To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV-Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.

Design and Implementation of a Virtual Sensor Network for Smart Waste Water Monitoring.

Monitoring and analysis of open air basins is a critical task in waste water plant management. These tasks generally require sampling waters at several hard to access points, be it real time with multiparametric sensor probes, or retrieving water samples. Full automation of these processes would require deploying hundreds (if not thousands) of fixed sensors, unless the sensors can be translated. This work proposes the utilization of robotized unmanned aerial vehicle (UAV) platforms to work as a virtual high density sensor network, which could analyze in real time or capture samples depending on the robotic UAV equipment. To check the validity of the concept, an instance of the robotized UAV platform has been fully designed and implemented. A multi-agent system approach has been used (implemented over a Robot Operating System, ROS, middleware layer) to define a software architecture able to deal with the different problems, optimizing modularity of the software; in terms of hardware, the UAV platform has been designed and built, as a sample capturing probe. A description on the main features of the multi-agent system proposed, its architecture, and the behavior of several components is discussed. The experimental validation and performance evaluation of the system components has been performed independently for the sake of safety: autonomous flight performance has been tested on-site; the accuracy of the localization technologies deemed as deployable options has been evaluated in controlled flights; and the viability of the sample capture device designed and built has been experimentally tested.

EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels.

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.

UAV Visual and Laser Sensors Fusion for Detection and Positioning in Industrial Applications.

This work presents a solution to localize Unmanned Autonomous Vehicles with respect to pipes and other cylindrical elements found in inspection and maintenance tasks both in industrial and civilian infrastructures. The proposed system exploits the different features of vision and laser based sensors, combining them to obtain accurate positioning of the robot with respect to the cylindrical structures. A probabilistic (RANSAC-based) procedure is used to segment possible cylinders found in the laser scans, and this is used as a seed to accurately determine the robot position through a computer vision system. The priors obtained from the laser scan registration help to solve the problem of determining the apparent contour of the cylinders. In turn this apparent contour is used in a degenerate quadratic conic estimation, enabling to visually estimate the pose of the cylinder.

Visual-Based SLAM Configurations for Cooperative Multi-UAV Systems with a Lead Agent: An Observability-Based Approach.

In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.

Cooperative Monocular-Based SLAM for Multi-UAV Systems in GPS-Denied Environments.

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.

Adaptation and psychometric properties of the spanish version of the yp-core (Young Person's Clinical Outcomes in Routine Evaluation).

INTRODUCTION: Given the increasing prevalence of mental health problems in the general population, it is indispensable to use assessment tools aimed to assess the outcome of therapeutic interventions in order to refine the process of psychological rehabilitation. METHOD: We describe the process of adaptation into Spanish and a first psychometric study of the Young Person's- Clinical Outcomes in Routine Evaluation (YP-CORE), an instrument designed to measure the outcome in terms of general distress of therapeutic interventions in young people (11-16 years). 104 adolescents participated in the clinical and 131 in the non-clinical samples. RESULTS: Analyses showed good levels of acceptability, adequate internal consistency and acceptable test-retest stability, with moderately high correlations between administrations. In addition, the instrument yielded significant correlations with all dimensions of the Youth Self Report, the highest being between both total scores. Crucially, discriminated between clinical and non-clinical samples and showed a small effect of age but a larger effect of gender, with higher scores for females. The Principal Component Analysis replicates the original structure. Cut-off scores to calculate the reliable and clinically significant change are provided. CONCLUSIONS: These results support initial use of the instrument though there are certain limitations that indicate the need for more research with larger and more representative samples, in which the psychometric properties of the instrument should be verified.

Vision-Based SLAM System for Unmanned Aerial Vehicles.

The present paper describes a vision-based simultaneous localization and mapping system to be applied to Unmanned Aerial Vehicles (UAVs). The main contribution of this work is to propose a novel estimator relying on an Extended Kalman Filter. The estimator is designed in order to fuse the measurements obtained from: (i) an orientation sensor (AHRS); (ii) a position sensor (GPS); and (iii) a monocular camera. The estimated state consists of the full state of the vehicle: position and orientation and their first derivatives, as well as the location of the landmarks observed by the camera. The position sensor will be used only during the initialization period in order to recover the metric scale of the world. Afterwards, the estimated map of landmarks will be used to perform a fully vision-based navigation when the position sensor is not available. Experimental results obtained with simulations and real data show the benefits of the inclusion of camera measurements into the system. In this sense the estimation of the trajectory of the vehicle is considerably improved, compared with the estimates obtained using only the measurements from the position sensor, which are commonly low-rated and highly noisy.

Human Collaborative Localization and Mapping in Indoor Environments with Non-Continuous Stereo.

A new approach to the monocular simultaneous localization and mapping (SLAM) problem is presented in this work. Data obtained from additional bearing-only sensors deployed as wearable devices is fully fused into an Extended Kalman Filter (EKF). The wearable device is introduced in the context of a collaborative task within a human-robot interaction (HRI) paradigm, including the SLAM problem. Thus, based on the delayed inverse-depth feature initialization (DI-D) SLAM, data from the camera deployed on the human, capturing his/her field of view, is used to enhance the depth estimation of the robotic monocular sensor which maps and locates the device. The occurrence of overlapping between the views of both cameras is predicted through geometrical modelling, activating a pseudo-stereo methodology which allows to instantly measure the depth by stochastic triangulation of matched points found through SIFT/SURF. Experimental validation is provided through results from experiments, where real data is captured as synchronized sequences of video and other data (relative pose of secondary camera) and processed off-line. The sequences capture indoor trajectories representing the main challenges for a monocular SLAM approach, namely, singular trajectories and close turns with high angular velocities with respect to linear velocities.

Monocular SLAM for autonomous robots with enhanced features initialization.

This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DI-D monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided.

A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system.

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

All-in-one aerial image enhancement network for forest scenes.

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

Family connections in the treatment of relatives of people with eating disorders and personality disorders: study protocol of a randomized control trial.

BACKGROUND: Eating disorders (EDs) are serious disorders that significantly affect not only the lives of patients, but also those of their family members who often experience high levels of burden, suffering and helplessness. If, in addition to ED, the patient has a personality disorder (PD), the psychological distress experienced by family members can be devastating. However, few treatments have been developed for family members of people with ED and PD. Family Connections (FC) is a programme that has been shown to be effective for family members of people with borderline personality disorder. The overall aims of this work are: (a) to adapt FC for application to family members of patients with BPD-PD (FC: ED-PD); (b) to analyse, in a randomised controlled clinical trial, the efficacy of this programme in a Spanish population, compared to a control condition consisting of treatment as usual optimised treatment (TAU-O); (c) to analyse the feasibility of the intervention protocol; (d) to analyse whether the changes that may occur in relatives are related to improvements in the family climate and/or improvements observed in patients; and (e) to analyse the perceptions and opinions of relatives and patients about the two intervention protocols. METHODS: The study uses a two-arm randomised controlled clinical trial with two experimental conditions: adaptation of FC programme (FC: ED-PD) or Treatment as usual optimised (TAU-O). Participants will be family members of patients who meet DSM-5 criteria for ED and PD or dysfunctional personality traits. Participants will be assessed before and after treatment and at one-year follow-up. The intention-to-treat principle will be used when analysing the data. DISCUSSION: The results obtained are expected to confirm the effectiveness of the programme and its good acceptance by family members. Trial registration ClinicalTrials.gov Identifier: NCT05404035. Accepted: May 2022.

Semi-supervised wildfire smoke detection based on smoke-aware consistency.

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

Further development of a scale of perceived expressed emotion and its evaluation in a sample of patients with eating disorders.

High expressed emotion (EE) as measured by the Camberwell Family Interview (CFI) predicts the course of eating disorders (ED). Despite its important contribution to the field, the CFI has two major limitations; it is time-consuming and it does not consider the patient's perspective. Obtaining the patient's view may help shed light on the dyadic nature of caregiver's EE and the patient's illness course. The objectives of our study of 77 patients with ED were to develop further a brief measure to assess the patients' perceptions of their caregivers' EE, the patient version of the Brief Dyadic Scale of Expressed Emotion (BDSEE), and to evaluate its psychometric properties. Three clearly separate factors were identified: perceived criticism, perceived emotional overinvolvement, and perceived warmth. The BDSEE also demonstrated good levels of reliability and construct validity. The BDSEE subscales are significantly related to other measures of the perceived family emotional climate and to the CFI, the gold standard in the field of EE. The clinical implications of the ED patients' perceptions of their caregivers' EE are discussed. Findings support the utility of the expanded BDSEE for both research and clinical practise in assessing the perspective of patients with ED.

Dyadic view of expressed emotion, stress, and eating disorder psychopathology.

Prevailing models of the association between expressed emotion (EE) and relapse conceptualize EE as a form of stress for patients. In eating disorders (ED), there is no research addressed to evaluate the degree to which patients feel stress due to their relatives' EE. It has been neither investigated how the EE and the subsequent stress relate to disordered behaviours and attitudes neither. Using a sample of 77 inpatients with ED, this study aimed to: (1) evaluate patients' reported level of stress as it relates to their caregivers' EE, particularly as associated with carer's criticism, emotional overinvolvement and warmth; (2) examine the associations of stress with the patients' perceptions (self-reported) and the caregivers' perspective (assessed by the Camberwell Family Interview) of the EE; and (3) study how the two views of EE (patients' and caregivers') and the stress due to EE relate to the ED symptoms. The findings indicate that patients judged their carers' critical stance as the most stressful, followed by emotional overinvolvement. Secondly, patients' perceptions of EE, whereas none of the interview indices focused on the caregivers' perspective, were associated to the stress and to the ED symptomatology. Additionally, the patients' stress due to criticism was positively related to the ED symptoms, while the stress associated with emotional overinvolvement and warmth was not. Clinical and research implications are discussed. Findings suggest attention to the ED patients' view of their family environment and support the utility of assessing their appraisals of EE.

Concurrent initialization for Bearing-Only SLAM.

Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. Early SLAM approaches focused on the use of range sensors as sonar rings or lasers. However, cameras have become more and more used, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. Unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a novel and robust method, called Concurrent Initialization, is presented which is inspired by having the complementary advantages of the Undelayed and Delayed methods that represent the most common approaches for addressing the problem. The key is to use concurrently two kinds of feature representations for both undelayed and delayed stages of the estimation. The simulations results show that the proposed method surpasses the performance of previous schemes.

Assessing relative post-release mortality for the transparent goby fishery: Environmental drivers and the utility of vitality metrics.

The target species (Aphia minuta and Pseudaphya ferreri) of the transparent goby fishery on Mallorca Island (Balearic Islands, western Mediterranean) are currently discarded when the maximum daily catch is exceeded or when the sorting process is unworkable. The mortality suffered by this discarded fraction remains unknown, although it may be important for resource management. Accordingly, the aims of this study were to (1) assess the environmental drivers of the relative post-release survival of the discarded target species and (2) test the correlations between post-release survival and two behavior-related variables (swimming speed and its increase after a stimulus, assessed via video recording). To do so, mortality of the target species from 47 hauls sampled under normal fishing boat operations was monitored when the gear was onboard and after a few hours. At the reference level (an average depth of 25.7 m and temperature of 15.7 ºC), the immediate survival was 99.9% (95% CI: 97.9 to 100%), but the estimated post-release survival decreased to 47.2% (33.8 to 65.8%). Relative post-release mortality doubled when the water temperature increased by 2.8 ºC or when the fishing depth increased up to 32 m. Furthermore, the swimming speed of the target species was significantly correlated with the estimated post-release survival; thus, this vitality metric may offer a promising strategy for more easily estimating post-release mortality in other fisheries.

A Randomized Trial of Deep Brain Stimulation to the Subcallosal Cingulate and Nucleus Accumbens in Patients with Treatment-Refractory, Chronic, and Severe Anorexia Nervosa: Initial Results at 6 Months of Follow Up.

BACKGROUND: The main objective of this study was to assess the safety and efficacy of deep brain stimulation (DBS) in patients with severe anorexia nervosa (AN). METHODS: Eight participants received active DBS to the subcallosal cingulate (SCC) or nucleus accumbens (NAcc) depending on comorbidities (affective or anxiety disorders, respectively) and type of AN. The primary outcome measure was body mass index (BMI). RESULTS: Overall, we found no significant difference (p = 0.84) between mean preoperative and postoperative (month 6) BMI. A BMI reference value (BMI-RV) was calculated. In patients that received preoperative inpatient care to raise the BMI, the BMI-RV was defined as the mean BMI value in the 12 months prior to surgery. In patients that did not require inpatient care, the BMI-RV was defined as the mean BMI in the 3-month period before surgery. This value was compared to the postoperative BMI (month 6), revealing a significant increase (p = 0.02). After 6 months of DBS, five participants showed an increase of ≥10% in the BMI-RV. Quality of life was improved (p = 0.03). Three cases presented cutaneous complications. CONCLUSION: DBS may be effective for some patients with severe AN. Cutaneous complications were observed. Longer term data are needed.