210 research outputs found
Calibration by correlation using metric embedding from non-metric similarities
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just
by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time
correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then
the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to
formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on
the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional
scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?)
and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature)
as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case,
on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric
measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric
information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional),
and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm
performs as theoretically predicted for all corner cases of the observability analysis
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System
Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes
Vision Sensors and Edge Detection
Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing
3D Scene Geometry Estimation from 360 Imagery: A Survey
This paper provides a comprehensive survey on pioneer and state-of-the-art 3D
scene geometry estimation methodologies based on single, two, or multiple
images captured under the omnidirectional optics. We first revisit the basic
concepts of the spherical camera model, and review the most common acquisition
technologies and representation formats suitable for omnidirectional (also
called 360, spherical or panoramic) images and videos. We then survey
monocular layout and depth inference approaches, highlighting the recent
advances in learning-based solutions suited for spherical data. The classical
stereo matching is then revised on the spherical domain, where methodologies
for detecting and describing sparse and dense features become crucial. The
stereo matching concepts are then extrapolated for multiple view camera setups,
categorizing them among light fields, multi-view stereo, and structure from
motion (or visual simultaneous localization and mapping). We also compile and
discuss commonly adopted datasets and figures of merit indicated for each
purpose and list recent results for completeness. We conclude this paper by
pointing out current and future trends.Comment: Published in ACM Computing Survey
Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System
Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes
Metric and appearance based visual SLAM for mobile robots
Simultaneous Localization and Mapping (SLAM) maintains autonomy for mobile robots and it has been studied extensively during the last two decades. It is the process of building the map of an unknown environment and determining the location of the robot using this map concurrently. Different kinds of sensors such as Global Positioning System (GPS), Inertial Measurement Unit (IMU), laser range finder and sonar are used for data acquisition in SLAM. In recent years, passive visual sensors are utilized in visual SLAM (vSLAM) problem because of their increasing ubiquity. This thesis is concerned with the metric and appearance-based vSLAM problems for mobile robots. From the point of view of metric-based vSLAM, a performance improvement technique is developed. Template matching based video stabilization and Harris corner detector are integrated. Extracting Harris corner features from stabilized video consistently increases the accuracy of the localization. Data coming from a video camera and odometry are fused in an Extended Kalman Filter (EKF) to determine the pose of the robot and build the map of the environment. Simulation results validate the performance improvement obtained by the proposed technique. Moreover, a visual perception system is proposed for appearance-based vSLAM and used for under vehicle classification. The proposed system consists of three main parts: monitoring, detection and classification. In the first part a new catadioptric camera system, where a perspective camera points downwards to a convex mirror mounted to the body of a mobile robot, is designed. Thanks to the catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part speeded up robust features (SURF) are used to detect the hidden objects that are under vehicles. Fast appearance based mapping algorithm (FAB-MAP) is then exploited for the classification of the means of transportations in the third part. Experimental results show the feasibility of the proposed system. The proposed solution is implemented using a non-holonomic mobile robot. In the implementations the bottom of the tables in the laboratory are considered as the under vehicles. A database that includes di erent under vehicle images is used. All the algorithms are implemented in Microsoft Visual C++ and OpenCV 2.4.4
Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems
We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis.
To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the âdirectâ tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS.
We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes
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