Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles

Autor: Davide Scaramuzza, Christian Laugier, Chiara Troiani, Agostino Martinelli
Přispěvatelé: Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Robotics and perception group [Zurich], Universität Zürich [Zürich] = University of Zurich (UZH), ANR-14-CE27-0009,VIMAD,navigation autonome des drones aériens avec la fusion des données visuels et inertielles(2014), University of Zurich, Troiani, Chiara, Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2015
Předmět:
Camera pose estimation
Computational complexity theory
10009 Department of Informatics
Computer science
General Mathematics
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
2207 Control and Systems Engineering
000 Computer science
knowledge & systems
Micro aerial vehicle
Computer Science::Robotics
Inertial measurement unit
Quadrotor
1706 Computer Science Applications
Outlier detection
[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]
Structure from motion
Computer vision
Vision-aided inertial navigation
Pose
Inertial navigation system
2600 General Mathematics
business.industry
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
GPS-denied navigation
Computer Science Applications
1712 Software
Control and Systems Engineering
Feature (computer vision)
Outlier
Onboard camera
Artificial intelligence
business
Algorithm
Software
Zdroj: Robotics and Autonomous Systems
Robotics and Autonomous Systems, Elsevier, 2015, 69, pp.80-97
Robotics and Autonomous Systems, 2015, 69, pp.80-97
ISSN: 0921-8890
1872-793X
Popis: This paper presents low computational-complexity methods for micro-aerial-vehicle localization in GPS-denied environments. All the presented algorithms rely only on the data provided by a single onboard camera and an Inertial Measurement Unit (IMU). This paper deals with outlier rejection and relative-pose estimation. Regarding outlier rejection, we describe two methods. The former only requires the observation of a single feature in the scene and the knowledge of the angular rates from an IMU, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. The latter requires the observation of at least two features, but it relaxes the hypothesis on the vehicle motion, being therefore suitable to tackle the outlier detection problem in the case of a 6DoF motion. We show also that if the camera is rigidly attached to the vehicle, motion priors from the IMU can be exploited to discard wrong estimations in the framework of a 2-point-RANSAC-based approach. Thanks to their inherent efficiency, the proposed methods are very suitable for resource-constrained systems. Regarding the pose estimation problem, we introduce a simple algorithm that computes the vehicle pose from the observation of three point features in a single camera image, once that the roll and pitch angles are estimated from IMU measurements. The proposed algorithm is based on the minimization of a cost function. The proposed method is very simple in terms of computational cost and, therefore, very suitable for real-time implementation. All the proposed methods are evaluated on both synthetic and real data. Low computational complexity methods for MAVs localization in GPS-denied environments and outlier detection.Platform: quadrotor equipped with a monocular camera and an IMU.Performance evaluation on both synthetic and real data.
Databáze: OpenAIRE
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