40,240 research outputs found
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
The ultimate goal of many image-based modeling systems is to render
photo-realistic novel views of a scene without visible artifacts. Existing
evaluation metrics and benchmarks focus mainly on the geometric accuracy of the
reconstructed model, which is, however, a poor predictor of visual accuracy.
Furthermore, using only geometric accuracy by itself does not allow evaluating
systems that either lack a geometric scene representation or utilize coarse
proxy geometry. Examples include light field or image-based rendering systems.
We propose a unified evaluation approach based on novel view prediction error
that is able to analyze the visual quality of any method that can render novel
views from input images. One of the key advantages of this approach is that it
does not require ground truth geometry. This dramatically simplifies the
creation of test datasets and benchmarks. It also allows us to evaluate the
quality of an unknown scene during the acquisition and reconstruction process,
which is useful for acquisition planning. We evaluate our approach on a range
of methods including standard geometry-plus-texture pipelines as well as
image-based rendering techniques, compare it to existing geometry-based
benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on
Graphics for revie
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
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
Computer algebra and operators
The symbolic computation of operator expansions is discussed. Some of the capabilities that prove useful when performing computer algebra computations involving operators are considered. These capabilities may be broadly divided into three areas: the algebraic manipulation of expressions from the algebra generated by operators; the algebraic manipulation of the actions of the operators upon other mathematical objects; and the development of appropriate normal forms and simplification algorithms for operators and their actions. Brief descriptions are given of the computer algebra computations that arise when working with various operators and their actions
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