934 research outputs found
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Visual localization, i.e., determining the position and orientation of a
vehicle with respect to a map, is a key problem in autonomous driving. We
present a multicamera visual inertial localization algorithm for large scale
environments. To efficiently and effectively match features against a pre-built
global 3D map, we propose a prioritized feature matching scheme for
multi-camera systems. In contrast to existing works, designed for monocular
cameras, we (1) tailor the prioritization function to the multi-camera setup
and (2) run feature matching and pose estimation in parallel. This
significantly accelerates the matching and pose estimation stages and allows us
to dynamically adapt the matching efforts based on the surrounding environment.
In addition, we show how pose priors can be integrated into the localization
system to increase efficiency and robustness. Finally, we extend our algorithm
by fusing the absolute pose estimates with motion estimates from a multi-camera
visual inertial odometry pipeline (VIO). This results in a system that provides
reliable and drift-less pose estimation. Extensive experiments show that our
localization runs fast and robust under varying conditions, and that our
extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure
Structureless Camera Motion Estimation of Unordered Omnidirectional Images
This work aims at providing a novel camera motion estimation pipeline from large collections of unordered omnidirectional images. In oder to keep the pipeline as general and flexible as possible, cameras are modelled as unit spheres, allowing to incorporate any central camera type. For each camera an unprojection lookup is generated from intrinsics, which is called P2S-map (Pixel-to-Sphere-map), mapping pixels to their corresponding positions on the unit sphere. Consequently the camera geometry becomes independent of the underlying projection model. The pipeline also generates P2S-maps from world map projections with less distortion effects as they are known from cartography. Using P2S-maps from camera calibration and world map projection allows to convert omnidirectional camera images to an appropriate world map projection in oder to apply standard feature extraction and matching algorithms for data association. The proposed estimation pipeline combines the flexibility of SfM (Structure from Motion) - which handles unordered image collections - with the efficiency of PGO (Pose Graph Optimization), which is used as back-end in graph-based Visual SLAM (Simultaneous Localization and Mapping) approaches to optimize camera poses from large image sequences. SfM uses BA (Bundle Adjustment) to jointly optimize camera poses (motion) and 3d feature locations (structure), which becomes computationally expensive for large-scale scenarios. On the contrary PGO solves for camera poses (motion) from measured transformations between cameras, maintaining optimization managable. The proposed estimation algorithm combines both worlds. It obtains up-to-scale transformations between image pairs using two-view constraints, which are jointly scaled using trifocal constraints. A pose graph is generated from scaled two-view transformations and solved by PGO to obtain camera motion efficiently even for large image collections. Obtained results can be used as input data to provide initial pose estimates for further 3d reconstruction purposes e.g. to build a sparse structure from feature correspondences in an SfM or SLAM framework with further refinement via BA.
The pipeline also incorporates fixed extrinsic constraints from multi-camera setups as well as depth information provided by RGBD sensors. The entire camera motion estimation pipeline does not need to generate a sparse 3d structure of the captured environment and thus is called SCME (Structureless Camera Motion Estimation).:1 Introduction
1.1 Motivation
1.1.1 Increasing Interest of Image-Based 3D Reconstruction
1.1.2 Underground Environments as Challenging Scenario
1.1.3 Improved Mobile Camera Systems for Full Omnidirectional Imaging
1.2 Issues
1.2.1 Directional versus Omnidirectional Image Acquisition
1.2.2 Structure from Motion versus Visual Simultaneous Localization and Mapping
1.3 Contribution
1.4 Structure of this Work
2 Related Work
2.1 Visual Simultaneous Localization and Mapping
2.1.1 Visual Odometry
2.1.2 Pose Graph Optimization
2.2 Structure from Motion
2.2.1 Bundle Adjustment
2.2.2 Structureless Bundle Adjustment
2.3 Corresponding Issues
2.4 Proposed Reconstruction Pipeline
3 Cameras and Pixel-to-Sphere Mappings with P2S-Maps
3.1 Types
3.2 Models
3.2.1 Unified Camera Model
3.2.2 Polynomal Camera Model
3.2.3 Spherical Camera Model
3.3 P2S-Maps - Mapping onto Unit Sphere via Lookup Table
3.3.1 Lookup Table as Color Image
3.3.2 Lookup Interpolation
3.3.3 Depth Data Conversion
4 Calibration
4.1 Overview of Proposed Calibration Pipeline
4.2 Target Detection
4.3 Intrinsic Calibration
4.3.1 Selected Examples
4.4 Extrinsic Calibration
4.4.1 3D-2D Pose Estimation
4.4.2 2D-2D Pose Estimation
4.4.3 Pose Optimization
4.4.4 Uncertainty Estimation
4.4.5 PoseGraph Representation
4.4.6 Bundle Adjustment
4.4.7 Selected Examples
5 Full Omnidirectional Image Projections
5.1 Panoramic Image Stitching
5.2 World Map Projections
5.3 World Map Projection Generator for P2S-Maps
5.4 Conversion between Projections based on P2S-Maps
5.4.1 Proposed Workflow
5.4.2 Data Storage Format
5.4.3 Real World Example
6 Relations between Two Camera Spheres
6.1 Forward and Backward Projection
6.2 Triangulation
6.2.1 Linear Least Squares Method
6.2.2 Alternative Midpoint Method
6.3 Epipolar Geometry
6.4 Transformation Recovery from Essential Matrix
6.4.1 Cheirality
6.4.2 Standard Procedure
6.4.3 Simplified Procedure
6.4.4 Improved Procedure
6.5 Two-View Estimation
6.5.1 Evaluation Strategy
6.5.2 Error Metric
6.5.3 Evaluation of Estimation Algorithms
6.5.4 Concluding Remarks
6.6 Two-View Optimization
6.6.1 Epipolar-Based Error Distances
6.6.2 Projection-Based Error Distances
6.6.3 Comparison between Error Distances
6.7 Two-View Translation Scaling
6.7.1 Linear Least Squares Estimation
6.7.2 Non-Linear Least Squares Optimization
6.7.3 Comparison between Initial and Optimized Scaling Factor
6.8 Homography to Identify Degeneracies
6.8.1 Homography for Spherical Cameras
6.8.2 Homography Estimation
6.8.3 Homography Optimization
6.8.4 Homography and Pure Rotation
6.8.5 Homography in Epipolar Geometry
7 Relations between Three Camera Spheres
7.1 Three View Geometry
7.2 Crossing Epipolar Planes Geometry
7.3 Trifocal Geometry
7.4 Relation between Trifocal, Three-View and Crossing Epipolar Planes
7.5 Translation Ratio between Up-To-Scale Two-View Transformations
7.5.1 Structureless Determination Approaches
7.5.2 Structure-Based Determination Approaches
7.5.3 Comparison between Proposed Approaches
8 Pose Graphs
8.1 Optimization Principle
8.2 Solvers
8.2.1 Additional Graph Solvers
8.2.2 False Loop Closure Detection
8.3 Pose Graph Generation
8.3.1 Generation of Synthetic Pose Graph Data
8.3.2 Optimization of Synthetic Pose Graph Data
9 Structureless Camera Motion Estimation
9.1 SCME Pipeline
9.2 Determination of Two-View Translation Scale Factors
9.3 Integration of Depth Data
9.4 Integration of Extrinsic Camera Constraints
10 Camera Motion Estimation Results
10.1 Directional Camera Images
10.2 Omnidirectional Camera Images
11 Conclusion
11.1 Summary
11.2 Outlook and Future Work
Appendices
A.1 Additional Extrinsic Calibration Results
A.2 Linear Least Squares Scaling
A.3 Proof Rank Deficiency
A.4 Alternative Derivation Midpoint Method
A.5 Simplification of Depth Calculation
A.6 Relation between Epipolar and Circumferential Constraint
A.7 Covariance Estimation
A.8 Uncertainty Estimation from Epipolar Geometry
A.9 Two-View Scaling Factor Estimation: Uncertainty Estimation
A.10 Two-View Scaling Factor Optimization: Uncertainty Estimation
A.11 Depth from Adjoining Two-View Geometries
A.12 Alternative Three-View Derivation
A.12.1 Second Derivation Approach
A.12.2 Third Derivation Approach
A.13 Relation between Trifocal Geometry and Alternative Midpoint Method
A.14 Additional Pose Graph Generation Examples
A.15 Pose Graph Solver Settings
A.16 Additional Pose Graph Optimization Examples
Bibliograph
3-D Scene Reconstruction from Aerial Imagery
3-D scene reconstructions derived from Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques were analyzed to determine the optimal reconnaissance flight characteristics suitable for target reconstruction. In support of this goal, a preliminary study of a simple 3-D geometric object facilitated the analysis of convergence angles and number of camera frames within a controlled environment. Reconstruction accuracy measurements revealed at least 3 camera frames and a 6 convergence angle were required to achieve results reminiscent of the original structure. The central investigative effort sought the applicability of certain airborne reconnaissance flight profiles to reconstructing ground targets. The data sets included images collected within a synthetic 3-D urban environment along circular, linear and s-curve aerial flight profiles equipped with agile and non-agile sensors. S-curve and dynamically controlled linear flight paths provided superior results, whereas with sufficient data conditioning and combination of orthogonal flight paths, all flight paths produced quality reconstructions under a wide variety of operational considerations
Angular variation as a monocular cue for spatial percepcion
Monocular cues are spatial sensory inputs which are picked up exclusively from one eye. They are in majority static features that
provide depth information and are extensively used in graphic art to create realistic representations of a scene. Since the spatial
information contained in these cues is picked up from the retinal image, the existence of a link between it and the theory of direct
perception can be conveniently assumed. According to this theory, spatial information of an environment is directly contained in the
optic array. Thus, this assumption makes possible the modeling of visual perception processes through computational approaches.
In this thesis, angular variation is considered as a monocular cue, and the concept of direct perception is adopted by a computer
vision approach that considers it as a suitable principle from which innovative techniques to calculate spatial information can be
developed.
The expected spatial information to be obtained from this monocular cue is the position and orientation of an object with respect to
the observer, which in computer vision is a well known field of research called 2D-3D pose estimation. In this thesis, the attempt to
establish the angular variation as a monocular cue and thus the achievement of a computational approach to direct perception is
carried out by the development of a set of pose estimation methods. Parting from conventional strategies to solve the pose
estimation problem, a first approach imposes constraint equations to relate object and image features. In this sense, two algorithms
based on a simple line rotation motion analysis were developed. These algorithms successfully provide pose information; however,
they depend strongly on scene data conditions. To overcome this limitation, a second approach inspired in the biological processes
performed by the human visual system was developed. It is based in the proper content of the image and defines a computational
approach to direct perception.
The set of developed algorithms analyzes the visual properties provided by angular variations. The aim is to gather valuable data
from which spatial information can be obtained and used to emulate a visual perception process by establishing a 2D-3D metric
relation. Since it is considered fundamental in the visual-motor coordination and consequently essential to interact with the
environment, a significant cognitive effect is produced by the application of the developed computational approach in environments
mediated by technology. In this work, this cognitive effect is demonstrated by an experimental study where a number of participants
were asked to complete an action-perception task. The main purpose of the study was to analyze the visual guided behavior in
teleoperation and the cognitive effect caused by the addition of 3D information. The results presented a significant influence of the
3D aid in the skill improvement, which showed an enhancement of the sense of presence.Las señales monoculares son entradas sensoriales capturadas exclusivamente por un
solo ojo que ayudan a la percepción de distancia o espacio. Son en su mayoría
características estáticas que proveen información de profundidad y son muy
utilizadas en arte gráfico para crear apariencias reales de una escena. Dado que la
información espacial contenida en dichas señales son extraídas de la retina, la
existencia de una relación entre esta extracción de información y la teoría de
percepción directa puede ser convenientemente asumida. De acuerdo a esta teoría, la
información espacial de todo le que vemos está directamente contenido en el arreglo
óptico. Por lo tanto, esta suposición hace posible el modelado de procesos de
percepción visual a través de enfoques computacionales. En esta tesis doctoral, la
variación angular es considerada como una señal monocular, y el concepto de
percepción directa adoptado por un enfoque basado en algoritmos de visión por
computador que lo consideran un principio apropiado para el desarrollo de nuevas
técnicas de cálculo de información espacial.
La información espacial esperada a obtener de esta señal monocular es la posición y
orientación de un objeto con respecto al observador, lo cual en visión por computador
es un conocido campo de investigación llamado estimación de la pose 2D-3D. En esta
tesis doctoral, establecer la variación angular como señal monocular y conseguir un
modelo matemático que describa la percepción directa, se lleva a cabo mediante el
desarrollo de un grupo de métodos de estimación de la pose. Partiendo de estrategias
convencionales, un primer enfoque implanta restricciones geométricas en ecuaciones
para relacionar características del objeto y la imagen. En este caso, dos algoritmos
basados en el análisis de movimientos de rotación de una línea recta fueron
desarrollados. Estos algoritmos exitosamente proveen información de la pose. Sin
embargo, dependen fuertemente de condiciones de la escena. Para superar esta
limitación, un segundo enfoque inspirado en los procesos biológicos ejecutados por el
sistema visual humano fue desarrollado. Está basado en el propio contenido de la
imagen y define un enfoque computacional a la percepción directa.
El grupo de algoritmos desarrollados analiza las propiedades visuales suministradas
por variaciones angulares. El propósito principal es el de reunir datos de importancia
con los cuales la información espacial pueda ser obtenida y utilizada para emular
procesos de percepción visual mediante el establecimiento de relaciones métricas 2D-
3D. Debido a que dicha relación es considerada fundamental en la coordinación
visuomotora y consecuentemente esencial para interactuar con lo que nos rodea, un
efecto cognitivo significativo puede ser producido por la aplicación de métodos de
L
estimación de pose en entornos mediados tecnológicamente. En esta tesis doctoral, este
efecto cognitivo ha sido demostrado por un estudio experimental en el cual un número
de participantes fueron invitados a ejecutar una tarea de acción-percepción. El
propósito principal de este estudio fue el análisis de la conducta guiada visualmente en
teleoperación y el efecto cognitivo causado por la inclusión de información 3D. Los
resultados han presentado una influencia notable de la ayuda 3D en la mejora de la
habilidad, así como un aumento de la sensación de presencia
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
This paper presents a novel method for fully automatic and convenient
extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally
printed chessboard. The proposed method is based on the 3D corner estimation of
the chessboard from the sparse point cloud generated by one frame scan of the
LiDAR. To estimate the corners, we formulate a full-scale model of the
chessboard and fit it to the segmented 3D points of the chessboard. The model
is fitted by optimizing the cost function under constraints of correlation
between the reflectance intensity of laser and the color of the chessboard's
patterns. Powell's method is introduced for resolving the discontinuity problem
in optimization. The corners of the fitted model are considered as the 3D
corners of the chessboard. Once the corners of the chessboard in the 3D point
cloud are estimated, the extrinsic calibration of the two sensors is converted
to a 3D-2D matching problem. The corresponding 3D-2D points are used to
calculate the absolute pose of the two sensors with Unified Perspective-n-Point
(UPnP). Further, the calculated parameters are regarded as initial values and
are refined using the Levenberg-Marquardt method. The performance of the
proposed corner detection method from the 3D point cloud is evaluated using
simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR
and a Ladybug3 camera under the proposed re-projection error metric,
qualitatively and quantitatively demonstrate the accuracy and stability of the
final extrinsic calibration parameters.Comment: 20 pages, submitted to the journal of Remote Sensin
Activity Monitoring Made Easier by Smart 360-degree Cameras
This paper proposes the use of smart 360-degree cameras for activity monitoring. By exploiting the geometric properties of these cameras and adopting off-the-shelf tracking algorithms adapted to equirectangular images, this paper shows how simple it becomes deploying a camera network, and detecting the presence of pedestrians in predefined regions of interest with minimal information on the camera, namely its height. The paper further shows that smart 360-degree cameras can enhance motion understanding in the environment and proposes a simple method to estimate the heatmap of the scene to highlight regions where pedestrians are more often present. Quantitative and qualitative results demonstrate the effectiveness of the proposed approach
Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking
The fruit fly Drosophila melanogaster is a widely used model organism in studies of genetics, developmental biology and biomechanics. One limitation for exploiting Drosophila as a model system for behavioral neurobiology is that measuring body kinematics during behavior is labor intensive and subjective. In order to quantify flight kinematics during different types of maneuvers, we have developed a visual tracking system that estimates the posture of the fly from multiple calibrated cameras. An accurate geometric fly model is designed using unit quaternions to capture complex body and wing rotations, which are automatically fitted to the images in each time frame. Our approach works across a range of flight behaviors, while also being robust to common environmental clutter. The tracking system is used in this paper to compare wing and body motion during both voluntary and escape take-offs. Using our automated algorithms, we are able to measure stroke amplitude, geometric angle of attack and other parameters important to a mechanistic understanding of flapping flight. When compared with manual tracking methods, the algorithm estimates body position within 4.4±1.3% of the body length, while body orientation is measured within 6.5±1.9 deg. (roll), 3.2±1.3 deg. (pitch) and 3.4±1.6 deg. (yaw) on average across six videos. Similarly, stroke amplitude and deviation are estimated within 3.3 deg. and 2.1 deg., while angle of attack is typically measured within 8.8 deg. comparing against a human digitizer. Using our automated tracker, we analyzed a total of eight voluntary and two escape take-offs. These sequences show that Drosophila melanogaster do not utilize clap and fling during take-off and are able to modify their wing kinematics from one wingstroke to the next. Our approach should enable biomechanists and ethologists to process much larger datasets than possible at present and, therefore, accelerate insight into the mechanisms of free-flight maneuvers of flying insects
Self-Calibration of Multi-Camera Systems for Vehicle Surround Sensing
Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data
- …