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Conventional camera calibration techniques rely on discrete reference points extracted from a set of input images. While these approaches have been applied successfully for a long time, omitting all image information apart from reference point positions at the initial stage of the calibration pipeline renders correct treatment of uncertainties difficult and gives rise to complications in timestamping...
This paper presents a solution for the extrinsic and intrinsic calibration of visual-inertial sensor systems. Calibration is formulated as a joint state and parameter estimation problem of a continuous-time system with discrete-time measurements. A maximum-likelihood estimator is derived to estimate the transform between cameras and inertial sensors, temporal alignment, and inertial sensor intrinsic...
As the applications of Micro Aerial Vehicles (MAVs) get more and more complex, and require highly dynamic motions, it becomes essential to have an accurate dynamic model of the MAV. Such a model can be used for reliable state estimation, control, and for realistic simulation. A good model requires accurate estimates of physical parameters of the system, which we aim to estimate from recorded flight...
An increasing number of robotic systems feature multiple inertial measurement units (IMUs). Due to competing objectives—either desired vicinity to the center of gravity when used in controls, or an unobstructed field of view when integrated in a sensor setup with an exteroceptive sensor for ego-motion estimation—individual IMUs are often mounted at considerable distance. As a result, they sense different...
Accurate visual-inertial localization and mapping systems require accurate calibration and good sensor error models. To this end, we present a simple offline method to automatically determine the parameters of inertial sensor noise models. The proposed methodology identifies noise processes across a large range of strength and time-scales, for example, weak gyroscope bias fluctuations buried in broadband...
The purpose of this paper is to evaluate the use of a micro aerial vehicle (MAV) for autonomous inspection and 3D reconstruction of underground mines. The goal is to manually fly an MAV equipped with cameras and a laser range sensor into a vertical shaft to collect data. This data can be used to evaluate the performance of the localization system as well as post processed to reconstruct a 3D model...
Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization...
This video presents experiments conducted within the final review meeting demonstration session of the AIRobots project. AIRobots started at 2010 and the final review meeting took place on 22 of March, 2013. The presented experiments cover a wide area of the challenges related with aerial industrial inspection. In particular, multiple test-cases related with both vision-based and contact-based inspection...
This work presents a small-scale Unmanned Aerial System (UAS) capable of performing inspection tasks in enclosed industrial environments. Vehicles with such capabilities have the potential to reduce human involvement in hazardous tasks and can minimize facility outage periods. The results presented generalize to UAS exploration tasks in almost any GPS-denied indoor environment. The contribution of...
Hearing is amongst the most important senses a modern robot must exhibit. Perceiving the acoustic world enables capabilities such as natural interaction with humans, interpreting spoken commands or the localization of victims during search and rescue tasks. Real-world robotic operations often take place in noisy, reverberant environments while requiring features such as source separation, accurate...
This work focuses on the use of MAVs for industrial inspection tasks. An efficient flight controller based on a model predictive control paradigm is developed. It allows for agile maneuvers in confined spaces while incorporating delays, saturations and inaccurate vehicle state estimates only available at low rate. The fast gradient method is used to solve the optimization problem and meet real-time...
In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multi-camera system in challenging indoor environments. It operates in real-time and employs information from two cameras with non-overlapping fields of view. Monocular Visual Odometry supplying up-to-scale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via...
This work presents a method for estimating the egomotion of an aerial vehicle in challenging industrial environments. It combines binocular visual and inertial cues in a tightly-coupled fashion and operates in real time on an embedded platform. An extended Kalman filter fuses measurements and makes motion estimation rely more on inertial data if visual feature constellation is degenerate. Errors in...
This work presents a method for estimating the egomotion of an aerial vehicle in challenging industrial environments. It combines binocular visual and inertial cues in a tightly-coupled fashion and operates in real time on an embedded platform. An extended Kalman filter fuses measurements and makes motion estimation rely more on inertial data if visual feature constellation is degenerate. Errors in...
In this paper, we consider power spectral density estimation of bandlimited, wide-sense stationary signals from sub-Nyquist sampled data. This problem has recently received attention from within the emerging field of cognitive radio for example, and solutions have been proposed that use ideas from compressed sensing and the theory of digital alias-free signal processing. Here we develop a compressed...
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