| 1. |
- Gustafsson, Fredrik, et al.
(författare)
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Sensor Fusion for Augmented Reality
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The problem of estimating the position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and a camera. The sensor fusion approach described in this contribution is based on nonlinear filtering using the measurements from these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing computation time and improving the performance in vision processing. A real-time implementation of a nonlinear filter is described, using a dynamic model for the 22 states, where 100 Hz inertial measurements and 12.5 Hz vision measurements are processed. An example where an industrial robot is used to move the sensor unit, possessing almost perfect precision and repeatability, is presented. The results show that position and orientation accuracy is sufficient for a number of augmented reality applications.
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| 2. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
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A Graphics Processing Unit Implementation of the Particle Filter
- 2007
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Ingår i: Proceedings of the 15th European Statistical Signal Processing Conference. - Linköping : European Association for Signal, Speech, and Image Processing. - 9788392134022 ; , s. 1639-1643
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Konferensbidrag (refereegranskat)abstract
- Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.
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| 3. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
-
Graphics Processing Unit Implementation of the Particle Filter
- 2006
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.
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| 4. |
- Hol, Jeroen, et al.
(författare)
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A New Algorithm for Calibrating a Combined Camera and IMU Sensor Unit
- 2008
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Ingår i: Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision. - Linköping : Linköping University Electronic Press. - 9781424422876 - 9781424422869 ; , s. 1857-1862
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper is concerned with the problem of estimating the relative translation and orientation between an inertial measurement unit and a camera which are rigidly connected. The key is to realise that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. Furthermore, covariance expressions are provided for all involved estimates. The experimental results shows that the method works well in practice.
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| 5. |
- Hol, Jeroen.D, et al.
(författare)
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Tightly Coupled UWB/IMU Pose Estimation
- 2009
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Ingår i: Proceedings of the IEEE International Conference on Ultra-Wideband (ICUWB). - Linköping : Linköping University Electronic Press. - 9781424429318 - 9781424429301 ; , s. 688-692
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose a 6DOF tracking system combining Ultra-Wideband measurements with low-cost MEMS inertial measurements. A tightly coupled system is developed which estimates position as well as orientation of the sensorunit while being reliable in case of multipath effects and NLOS conditions. The experimental results show robust and continuous tracking in a realistic indoor positioning scenario.
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| 6. |
- Hol, Jeroen, et al.
(författare)
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Modeling and Calibration of Inertial and Vision Sensors
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The method is based on a physical model which can also be used in solving, for example, sensor fusion problems. The experimental results show that the method works well in practice, both for perspective and spherical cameras.
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| 7. |
- Hol, Jeroen, et al.
(författare)
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On Resampling Algorithms for Particle Filters
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms with respect to their resampling quality and computational complexity.Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in terms of resampling quality and computational complexity.
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| 8. |
- Hol, Jeroen, 1981-, et al.
(författare)
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Relative Pose Calibration of a Spherical Camera and an IMU
- 2008
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Ingår i: Proceedings of the 7th IEEE and ACM International Symposium on Mixed and Augmented Reality. - Linköping : Linköping University Electronic Press. - 9781424428595 - 9781424428403 ; , s. 21-24
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with the problem of estimating the relative translation and orientation of an inertial measurement unit and a spherical camera, which are rigidly connected. The key is to realize that this problem is in fact an instance of a standard problem within the area of system identification, referred to as a gray-box problem. We propose a new algorithm for estimating the relative translation and orientation, which does not require any additional hardware, except a piece of paper with a checkerboard pattern on it. The experimental results show that the method works well in practice.
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| 9. |
- Hol, Jeroen, et al.
(författare)
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Robust Real-Time Tracking by Fusing Measurements from Inertial and Vision Sensors
- 2007
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Ingår i: Journal of Real-Time Image Processing. - Linköping : Springer. - 1861-8200 .- 1861-8219. ; 2:2-3, s. 149-160
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Tidskriftsartikel (refereegranskat)abstract
- The problem of estimating and predicting position and orientation (pose) of a camera is approached by fusing measurements from inertial sensors (accelerometers and rate gyroscopes) and vision. The sensor fusion approach described in this contribution is based on non-linear filtering of these complementary sensors. This way, accurate and robust pose estimates are available for the primary purpose of augmented reality applications, but with the secondary effect of reducing computation time and improving the performance in vision processing. A real-time implementation of a multi-rate extended Kalman filter is described, using a dynamic model with 22 states, where 12.5 Hz correspondences from vision and 100 Hz inertial measurements are processed. An example where an industrial robot is used to move the sensor unit is presented. The advantage with this configuration is that it provides ground truth for the pose, allowing for objective performance evaluation. The results show that we obtain an absolute accuracy of 2 cm in position and 1° in orientation.
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| 10. |
- Hol, Jeroen, et al.
(författare)
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Sensor Fusion for Augmented Reality
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In Augmented Reality (AR), the position and orientation of the camera have to be estimated with high accuracy and low latency. This nonlinear estimation problem is studied in the present paper. The proposed solution makes use of measurements from inertial sensors and computer vision. These measurements are fused using a Kalman filtering framework, incorporating a rather detailed model for the dynamics of the camera. Experiments show that the resulting filter provides good estimates of the camera motion, even during fast movements.
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