Circular Laser/Camera-Based Attitude and Altitude Estimation: Minimal and Robust Solutions

In this paper, we propose a vision-based system to localize mobile robots in large industrial environments. Our contributions rely on the use of fisheye cameras to have a large field of view and the associated algorithms. We propose several calibration methods and evaluate them with a ground-truth obtained by a motion capture system. In these experiments, we also evaluate the influence of the parameters as the number of points used for calibration, or the influence of the accuracy of these points. Our system is then experimented in a real industrial environment, where we localize blue an ROV (Remotely Operated underwater Vehicle) in a basin. As shown is this paper, this system can also localize wheeled mobile robots in the same way. Keywords vision-based localization • fisheye cameras • robust pose estimation • extrinsic calibration 1 Introduction Robots have long been used in industry, but they have long been confined to automated production tasks. In recent years, with the advent of the Factory of the Future (FoF) or Industry

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“…We thus only consider pitch and roll angles. The Denavit-Hartenberg [5,12] parameterization of R world cam with these two angles leads to…”

Section: Auto-calibration By Moving the Rovmentioning

confidence: 99%

A Vision-Based System for Robot Localization in Large Industrial Environments

Boutteau

Rossi

Qin

et al. 2019

J Intell Robot Syst

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“…Boutteau et al [17] propose a system based on circular laser source and an RGB camera for estimating the attitude and altitude of a UAV. The authors develop a geometric modeling to estimate the pose at the scale of the system and relative to the plane onto which the pattern is projected, unable to estimate the correct altitude in presence of ground obstacles.…”

Section: Vision Based Techniquesmentioning

confidence: 99%

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

et al. 2018

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This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

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Circular Laser/Camera-Based Attitude and Altitude Estimation: Minimal and Robust Solutions

Cited by 2 publications

References 22 publications

A Vision-Based System for Robot Localization in Large Industrial Environments

A Vision-Based System for Robot Localization in Large Industrial Environments

Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

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