SANet: Scene agnostic network for camera localization

This thesis presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other. Despite recent advancement in learning based methods with scene coordinate regression, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly. Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6 DoF camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance among methods working for arbitrary scenes without retraining or adaptation

Bond graph modeling of centrifugal compression systems

A novel approach to model unsteady fluid dynamics in a compressor network by using a bond graph is presented. The model is intended in particular for compressor control system development. First, we develop a bond graph model of a single compression system. Bond graph modeling offers a different perspective to previous work by modeling the compression system based on energy flow instead of fluid dynamics. Analyzing the bond graph model explains the energy flow during compressor surge. Two principal solutions for compressor surge problem are identified: upstream energy injection and downstream energy dissipation. Both principal solutions are verified in bond graph modelings of single compression system equipped with a surge avoidance system (SAS) and single compression system equipped with an active control system. Moreover, the bond graph model of single compressor equipped with SAS is able to show the effect of recycling flow to the compressor upstream states which improves the current available model. The bond graph model of a single compression system is then used as the base model and combined to build compressor network models. Two compressor networks are modeled: serial compressors and parallel compressors. Simulation results show the surge conditions in both compressor networks.© SAGE. This is the authors’ accepted and refereed manuscript to the article

Towards a science and practice of resilience in the face of pain

The primary objective of this paper is to discuss how a resilience approach to (chronic) pain may advance our current understanding of (mal)adaptation to pain. Different resilience perspectives are described, and future challenges for research, prevention and treatment of (chronic) pain are discussed. Literature searches were performed in Web of Science and PubMed to identify relevant literature on risk and resilience in the context of pain. Resilience can be best defined as the ability to restore and sustain living a fulfilling life in the presence of pain. The Psychological Flexibility Model, the Broaden-and-Build Theory, and Self-Determination Theory are described as theories that may provide insight into resilience within the context of (chronic) pain. We describe how a resilience paradigm shifts the outcomes to pursue in pain research and intervention and argue the need for including positive outcomes in addition to negative outcomes. Psychological flexibility, positive affect and basic psychological needs satisfaction are described as potentially important resilience mechanisms with the potential to target both sustainability and recovery from pain. A resilience approach to chronic pain may have important implications for the prevention and treatment of chronic pain problems, as it may give specific indications on how to empower patients to continue living a fulfilling life (in the presence of pain)

Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner. We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201

Precompetitive achievement goals, stress appraisals, emotions, and coping among athletes

Grounded in Lazarus’ (1991, 1999, 2000) Cognitive-Motivational-Relational theory of emotions, we tested a model of achievement goals, stress appraisals, emotions, and coping. We predicted that pre-competitive achievement goals would be associated with appraisals; appraisals with emotions; and emotions with coping in our model. The mediating effects of emotions among the overall sample of 827 athletes and two stratified random sub-samples were also explored. The results of this study support our proposed model in the overall sample and the stratified sub-samples. Further, emotion mediated the relationship between appraisal and coping. Mediation analyses revealed that there were indirect effects of pleasant and unpleasant emotions, which indicates the importance of examining multiple emotions to reveal a more accurate representation of the overall stress process. Our findings indicate that both appraisals and emotions are just as important in shaping coping

Robot swarming applications

This paper discusses the different modes of operation of a swarm of robots: (i) non-communicative swarming, (ii) communicative swarming, (iii) networking, (iv) olfactory-based navigation and (v) assistive swarming. I briefly present the state of the art in swarming and outline the major techniques applied for each mode of operation and discuss the related problems and expected results

J-MOD2^{2}: Joint Monocular Obstacle Detection and Depth Estimation

In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model