arXiv subjects

Kostas Alexis

Publications and source records attributed to Kostas Alexis.

At least 91 records · Page 5Linked to original sources

arXiv2021

Resource-aware Online Parameter Adaptation for Computationally-constrained Visual-Inertial Navigation Systems

In this paper, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computationally expensive processes. To achieve this objective, the algorithm proposes selected changes in the vision front-end and optimization back-end of visual-inertial odometry algorithms, both prior to execution and in real-time based on an online profiling of available resources. The method also utilizes information from the motion dynamics experienced by the system to manipulate parameters online. The general policy is demonstrated on three established algorithms, namely S-MSCKF, VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset. The proposed approach achieved comparable performance at a fraction of the original computational cost.

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arXiv2020

Visual-Thermal Camera Dataset Release and Multi-Modal Alignment without Calibration Information

This report accompanies a dataset release on visual and thermal camera data and details a procedure followed to align such multi-modal camera frames in order to provide pixel-level correspondence between the two without using intrinsic or extrinsic calibration information. To achieve this goal we benefit from progress in the domain of multi-modal image alignment and specifically employ the Mattes Mutual Information Metric to guide the registration process. In the released dataset we release both the raw visual and thermal camera data, as well as the aligned frames, alongside calibration parameters with the goal to better facilitate the investigation on common local/global features across such multi-modal image streams.

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arXiv2020

Appendix for the Motion Primitives-based Path Planning for Fast and Agile Exploration Method

This manuscript presents enhancements on our motion-primitives exploration path planning method for agile exploration using aerial robots. The method now further integrates a global planning layer to facilitate reliable large-scale exploration. The implemented bifurcation between local and global planning allows for efficient exploration combined with the ability to plan within very large environments, while also ensuring safe and timely return-to-home. A new set of simulation studies and experimental results are presented to demonstrate the new improvements and enhancements. The method is available open source as a Robot Operating System (ROS) package.

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arXiv2020

Model Predictive Control for Micro Aerial Vehicles: A Survey

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

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arXiv2020

Anomalous Motion Detection on Highway Using Deep Learning

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.

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arXiv2020

Autonomous Aerial Robotic Surveying and Mapping with Application to Construction Operations

In this paper we present an overview of the methods and systems that give rise to a flying robotic system capable of autonomous inspection, surveying, comprehensive multi-modal mapping and inventory tracking of construction sites with high degree of systematicity. The robotic system can operate assuming either no prior knowledge of the environment or by integrating a prior model of it. In the first case, autonomous exploration is provided which returns a high fidelity $3\textrm{D}$ map associated with color and thermal vision information. In the second case, the prior model of the structure can be used to provide optimized and repetitive coverage paths. The robot delivers its mapping result autonomously, while simultaneously being able to detect and localize objects of interest thus supporting inventory tracking tasks. The system has been field verified in a collection of environments and has been tested inside a construction project related to public housing.

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arXiv2020

Towards a Science of Resilient Robotic Autonomy

This discussion paper aims to support the argument process for the need to develop a comprehensive science of resilient robotic autonomy. Resilience and its key characteristics relating to robustness, redundancy, and resourcefulness are discussed, followed by a selected - but not exhaustive - list of research themes and domains that are crucial to facilitate resilient autonomy. Last but not least, an outline of possible directions of a new and enhanced design paradigm in robotics is presented. This manuscript is intentionally short and abstract. It serves to open the discussion and raise questions. The answers will necessarily be found in the actual process of conducting research by the community and in the framework of introducing robotics in an ever increasing set of real-life use cases. Its current form is based on thoughts identified within the ongoing experience of conducting research for robotic systems to gain autonomy in certain types of extreme environments such as subterranean settings, nuclear facilities, agriculture areas, and long-term off-road deployments. The very context of this document will be subject to change and it will be iteratively revisited.

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arXiv2019

The Reconfigurable Aerial Robotic Chain: Shape and Motion Planning

This paper presents the design concept, modeling and motion planning solution for the aerial robotic chain. This design represents a configurable robotic system of systems, consisting of multi-linked micro aerial vehicles that simultaneously presents the ability to cross narrow sections, morph its shape, ferry significant payloads, offer the potential of distributed sensing and processing, and allow system extendability. We contribute an approach to address the motion planning problem of such a connected robotic system of systems, making full use of its reconfigurable nature, to find collision free paths in a fast manner despite the increased number of degrees of freedom. The presented approach exploits a library of aerial robotic chain configurations, optimized either for cross-section size or sensor coverage, alongside a probabilistic strategy to sample random shape configurations that may be needed to facilitate continued collision-free navigation. Evaluation studies in simulation involve traversal of constrained and obstacle-laden environments, having narrow corridors and cross sections.

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arXiv2019

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR.

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arXiv2019

Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a set of hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown to perform reliably in challenging visually-degraded environments using RGB-D information from a lightweight and low-cost sensor and data from an IMU.

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arXiv2019

Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

With an ever-widening domain of aerial robotic applications, including many mission critical tasks such as disaster response operations, search and rescue missions and infrastructure inspections taking place in GPS-denied environments, the need for reliable autonomous operation of aerial robots has become crucial. Operating in GPS-denied areas aerial robots rely on a multitude of sensors to localize and navigate. Visible spectrum cameras are the most commonly used sensors due to their low cost and weight. However, in environments that are visually-degraded such as in conditions of poor illumination, low texture, or presence of obscurants including fog, smoke and dust, the reliability of visible light cameras deteriorates significantly. Nevertheless, maintaining reliable robot navigation in such conditions is essential. In contrast to visible light cameras, thermal cameras offer visibility in the infrared spectrum and can be used in a complementary manner with visible spectrum cameras for robot localization and navigation tasks, without paying the significant weight and power penalty typically associated with carrying other sensors. Exploiting this fact, in this work we present a multi-sensor fusion algorithm for reliable odometry estimation in GPS-denied and degraded visual environments. The proposed method utilizes information from both the visible and thermal spectra for landmark selection and prioritizes feature extraction from informative image regions based on a metric over spatial entropy. Furthermore, inertial sensing cues are integrated to improve the robustness of the odometry estimation process. To verify our solution, a set of challenging experiments were conducted inside a) an obscurant filed machine shop-like industrial environment, as well as b) a dark subterranean mine in the presence of heavy airborne dust.

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arXiv2019

Keyframe-based Direct Thermal-Inertial Odometry

This paper proposes an approach for fusing direct radiometric data from a thermal camera with inertial measurements to extend the robotic capabilities of aerial robots for navigation in GPS-denied and visually degraded environments in the conditions of darkness and in the presence of airborne obscurants such as dust, fog and smoke. An optimization based approach is developed that jointly minimizes the re-projection error of 3D landmarks and inertial measurement errors. The developed solution is extensively verified against both ground-truth in an indoor laboratory setting, as well as inside an underground mine under severely visually degraded conditions.

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arXiv2019

Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments

For robotic inspection tasks in known environments fiducial markers provide a reliable and low-cost solution for robot localization. However, detection of such markers relies on the quality of RGB camera data, which degrades significantly in the presence of visual obscurants such as fog and smoke. The ability to navigate known environments in the presence of obscurants can be critical for inspection tasks especially, in the aftermath of a disaster. Addressing such a scenario, this work proposes a method for the design of fiducial markers to be used with thermal cameras for the pose estimation of aerial robots. Our low cost markers are designed to work in the long wave infrared spectrum, which is not affected by the presence of obscurants, and can be affixed to any object that has measurable temperature difference with respect to its surroundings. Furthermore, the estimated pose from the fiducial markers is fused with inertial measurements in an extended Kalman filter to remove high frequency noise and error present in the fiducial pose estimates. The proposed markers and the pose estimation method are experimentally evaluated in an obscurant filled environment using an aerial robot carrying a thermal camera.

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arXiv2018

Lévy Flight Foraging Hypothesis-based Autonomous Memoryless Search Under Sparse Rewards

Autonomous robots are commonly tasked with the problem of area exploration and search for certain targets or artifacts of interest to be tracked. Traditionally, the problem formulation considered is that of complete search and thus - ideally - identification of all targets of interest. An important problem however which is not often addressed is that of time-efficient memoryless search under sparse rewards that may be worth visited any number of items. In this paper we specifically address the largely understudied problem of optimizing the "time-of-arrival" or "time-of-detection" to robotically search for sparsely distributed rewards (detect targets of interest) within large-scale environments and subject to memoryless exploration. At the core of the proposed solution is the fact that a search-based Lévy walk consisting of a constant velocity search following a Lévy flight path is optimal for searching sparse and randomly distributed target regions in the lack of map memory. A set of results accompany the presentation of the method, demonstrate its properties and justify the purpose of its use towards large-scale area exploration autonomy.

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arXiv2018

Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving

In this research we present a novel algorithm for background subtraction using a moving camera. Our algorithm is based purely on visual information obtained from a camera mounted on an electric bus, operating in downtown Reno which automatically detects moving objects of interest with the view to provide a fully autonomous vehicle. In our approach we exploit the optical flow vectors generated by the motion of the camera while keeping parameter assumptions a minimum. At first, we estimate the Focus of Expansion, which is used to model and simulate 3D points given the intrinsic parameters of the camera, and perform multiple linear regression to estimate the regression equation parameters and implement on the real data set of every frame to identify moving objects. We validated our algorithm using data taken from a common bus route.

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arXiv2018

Design and Control of an Aerial Manipulator for Contact-based Inspection

Manipulator dynamics, external forces and moments raise issues in stability and efficient control during aerial manipulation. Additionally, multirotor Micro Aerial Vehicles impose stringent limits on payload, actuation and system states. In view of these challenges, this work addressed the design and control of a 3-DoF serial aerial manipulator for contact inspection. A lightweight design with sufficient dexterous workspace for NDT (Non-Destructive Testing) inspection is presented. This operation requires the regulation of normal force on the inspected point. Contact dynamics have been discussed along with a simulation of the closed-loop dynamics during contact. The simulated controller preserves inherent system nonlinearities and uses a passivity approach to ensure the convergence of error to zero. A transition scheme from free-flight to contact was developed along with the hardware and software frameworks for implementation. This paper concludes with important drawbacks and prospects.

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arXiv2018

Visual-Inertial Odometry-enhanced Geometrically Stable ICP for Mapping Applications using Aerial Robots

This paper presents a visual-inertial odometry-enhanced geometrically stable Iterative Closest Point (ICP) algorithm for accurate mapping using aerial robots. The proposed method employs a visual-inertial odometry framework in order to provide robust priors to the ICP step and calculate the overlap among point clouds derived from an onboard time-of-flight depth sensor. Within the overlapping parts of the point clouds, the method samples points such that the distribution of normals among them is as large as possible. As different geometries and sensor trajectories will influence the performance of the alignment process, evaluation of the expected geometric stability of the ICP step is conducted. It is only when this test is successful that the matching, outlier rejection, and minimization of the error metric ICP steps are conducted and the new relative translation and rotational components are estimated, otherwise the system relies on the visual-inertial odometry transformation estimates. The proposed strategy was evaluated within handheld, automated and fully autonomous exploration and mapping missions using a small aerial robot and was shown to provide robust results of superior quality at an affordable increase of the computational load.

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arXiv2017

Aerial Drop of Robots and Sensors for Optimal Area Coverage

The problem of rapid optimal coverage through the distribution a team of robots or static sensors via means of aerial drop is the topic of this work. Considering a nonholonomic (fixed-wing) aerial robot that corresponds to the carrier of a set of small holonomic (rotorcraft) aerial robots as well as static modules that are all equipped with a camera sensor, we address the problem of selecting optimal aerial drop times and configurations while the motion capabilities of the small aerial robots are also exploited to further survey their area of responsibility until they hit the ground. The overall solution framework consists of lightweight path-planning algorithms that can run on virtually any processing unit that might be available on-board. Evaluation studies in simulation as well as a set of elementary experiments that prove the validity of important assumptions illustrate the potential of the approach.

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