432 research outputs found
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201
Accurate and Interactive Visual-Inertial Sensor Calibration with Next-Best-View and Next-Best-Trajectory Suggestion
Visual-Inertial (VI) sensors are popular in robotics, self-driving vehicles,
and augmented and virtual reality applications. In order to use them for any
computer vision or state-estimation task, a good calibration is essential.
However, collecting informative calibration data in order to render the
calibration parameters observable is not trivial for a non-expert. In this
work, we introduce a novel VI calibration pipeline that guides a non-expert
with the use of a graphical user interface and information theory in collecting
informative calibration data with Next-Best-View and Next-Best-Trajectory
suggestions to calibrate the intrinsics, extrinsics, and temporal misalignment
of a VI sensor. We show through experiments that our method is faster, more
accurate, and more consistent than state-of-the-art alternatives. Specifically,
we show how calibrations with our proposed method achieve higher accuracy
estimation results when used by state-of-the-art VI Odometry as well as VI-SLAM
approaches. The source code of our software can be found on:
https://github.com/chutsu/yac.Comment: 8 pages, 11 figures, IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2023
Towards Visual Ego-motion Learning in Robots
Many model-based Visual Odometry (VO) algorithms have been proposed in the
past decade, often restricted to the type of camera optics, or the underlying
motion manifold observed. We envision robots to be able to learn and perform
these tasks, in a minimally supervised setting, as they gain more experience.
To this end, we propose a fully trainable solution to visual ego-motion
estimation for varied camera optics. We propose a visual ego-motion learning
architecture that maps observed optical flow vectors to an ego-motion density
estimate via a Mixture Density Network (MDN). By modeling the architecture as a
Conditional Variational Autoencoder (C-VAE), our model is able to provide
introspective reasoning and prediction for ego-motion induced scene-flow.
Additionally, our proposed model is especially amenable to bootstrapped
ego-motion learning in robots where the supervision in ego-motion estimation
for a particular camera sensor can be obtained from standard navigation-based
sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
experiments, we show the utility of our proposed approach in enabling the
concept of self-supervised learning for visual ego-motion estimation in
autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures,
2 table
Real Time Stereo Cameras System Calibration Tool and Attitude and Pose Computation with Low Cost Cameras
The Engineering in autonomous systems has many strands. The area in which this work falls, the artificial vision, has become one of great interest in multiple contexts and focuses on robotics. This work seeks to address and overcome some real difficulties encountered when developing technologies with artificial vision systems which are, the calibration process and pose computation of robots in real-time. Initially, it aims to perform real-time camera intrinsic (3.2.1) and extrinsic (3.3) stereo camera systems calibration needed to the main goal of this work, the real-time pose (position and orientation) computation of an active coloured target with stereo vision systems.
Designed to be intuitive, easy-to-use and able to run under real-time applications, this work was developed for use either with low-cost and easy-to-acquire or more complex and high resolution stereo vision systems in order to compute all the parameters inherent to this same system such as the intrinsic values of each one of the cameras and the extrinsic matrices computation between both cameras. More oriented towards the underwater environments, which are very dynamic and computationally more complex due to its particularities such as light reflections.
The available calibration information, whether generated by this tool or loaded configurations from other tools allows, in a simplistic way, to proceed to the calibration of an environment colorspace and the detection parameters of a specific target with active visual markers (4.1.1), useful within unstructured environments. With a calibrated system and environment, it is possible to detect and compute, in real time, the pose of a target of interest. The combination of position and orientation or attitude is referred as the pose of an object.
For performance analysis and quality of the information obtained, this tools are compared with others already existent.A engenharia de sistemas autónomos actua em diversas vertentes. Uma delas, a visão artificial, em que este trabalho assenta, tornou-se uma das de maior interesse em múltiplos contextos e focos na robótica. Assim, este trabalho procura abordar e superar algumas dificuldades encontradas aquando do desenvolvimento de tecnologias baseadas na visão artificial. Inicialmente, propõe-se a fornecer ferramentas para realizar as calibrações necessárias de intrínsecos (3.2.1) e extrínsecos (3.3) de sistemas de visão stereo em tempo real para atingir o objectivo principal, uma ferramenta de cálculo da posição e orientação de um alvo activo e colorido através de sistemas de visão stereo.
Desenhadas para serem intuitivas, fáceis de utilizar e capazes de operar em tempo real, estas ferramentas foram desenvolvidas tendo em vista a sua integração quer com camaras de baixo custo e aquisição fácil como com camaras mais complexas e de maior resolução. Propõem-se a realizar a calibração dos parâmetros inerentes ao sistema de visão stereo como os intrínsecos de cada uma das camaras e as matrizes de extrínsecos que relacionam ambas as camaras. Este trabalho foi orientado para utilização em meio subaquático onde se presenciam ambientes com elevada dinâmica visual e maior complexidade computacional devido `a suas particularidades como reflexões de luz e má visibilidade.
Com a informação de calibração disponível, quer gerada pelas ferramentas fornecidas, quer obtida a partir de outras, pode ser carregada para proceder a uma calibração simplista do espaço de cor e dos parâmetros de deteção de um alvo específico com marcadores ativos coloridos (4.1.1). Estes marcadores são ´uteis em ambientes não estruturados.
Para análise da performance e qualidade da informação obtida, as ferramentas de calibração e cálculo de pose (posição e orientação), serão comparadas com outras já existentes
- …