| 1. |
- Batstone, Kenneth, et al.
(författare)
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Collaborative merging of radio SLAM maps in view of crowd-sourced data acquisition and big data
- 2019
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Ingår i: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 807-813
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Konferensbidrag (refereegranskat)abstract
- Indoor localization and navigation is a much researched and difficult problem. The best solutions, usually use expensive specialized equipment and/or prior calibration of some form. To the average person with smart or Internet-Of-Things devices, these solutions are not feasible, particularly in large scales. With hardware advancements making Ultra-Wideband devices more accurate and low powered, this unlocks the potential of having such devices in commonplace around factories and homes, enabling an alternative method of navigation. Therefore, indoor anchor calibration becomes a key problem in order to implement these devices efficiently and effectively. In this paper, we present a method to fuse radio SLAM (also known as Time-Of-Arrival self-calibration) maps together in a linear way. In doing so we are then able to collaboratively calibrate the anchor positions in 3D to native precision of the devices. Furthermore, we introduce an automatic scheme to determine which of the maps are best to use to further improve the anchor calibration and its robustness but also show which maps could be discarded. Additionally, when a map is fused in a linear way, it is a very computationally cheap process and produces a reasonable map which is required to push for crowd-sourced data acquisition.
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| 2. |
- Li, Zhongguo, et al.
(författare)
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Template based human pose and shape estimation from a single RGB-D image
- 2019
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Ingår i: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 574-581
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Konferensbidrag (refereegranskat)abstract
- Estimating the 3D model of the human body is needed for many applications. However, this is a challenging problem since the human body inherently has a high complexity due to self-occlusions and articulation. We present a method to reconstruct the 3D human body model from a single RGB-D image. 2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D joint points are calculated using the depth image. Then, we propose to utilize both 2D and 3D joint points, which provide more information, to fit a parametric body model (SMPL). This is implemented through minimizing an objective function, which measures the difference of the joint points between the observed model and the parametric model. The pose and shape parameters of the body are obtained through optimization and the final 3D model is estimated. The experiments on synthetic data and real data demonstrate that our method can estimate the 3D human body model correctly.
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| 3. |
- Örnhag, Marcus Valtonen, et al.
(författare)
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Fast Non-minimal Solvers for Planar Motion Compatible Homographies
- 2019
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Ingår i: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 40-51
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel polynomial constraint for homographies compatible with the general planar motion model. In this setting, compatible homographies have five degrees of freedom-instead of the general case of eight degrees of freedom-and, as a consequence, a minimal solver requires 2.5 point correspondences. The existing minimal solver, however, is computationally expensive, and we propose using non-minimal solvers, which significantly reduces the execution time of obtaining a compatible homography, with accuracy and robustness comparable to that of the minimal solver. The proposed solvers are compared with the minimal solver and the traditional 4-point solver on synthetic and real data, and demonstrate good performance, in terms of speed and accuracy. By decomposing the homographies obtained from the different methods, it is shown that the proposed solvers have future potential to be incorporated in a complete Simultaneous Localization and Mapping (SLAM) framework.
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| 4. |
- Örnhag, Marcus Valtonen, et al.
(författare)
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Planar motion bundle adjustment
- 2019
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Ingår i: ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. - : SCITEPRESS - Science and Technology Publications. - 9789897583513 ; , s. 24-31
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Konferensbidrag (refereegranskat)abstract
- In this paper we consider trajectory recovery for two cameras directed towards the floor, and which are mounted rigidly on a mobile platform. Previous work for this specific problem geometry has focused on locally minimising an algebraic error between inter-image homographies to estimate the relative pose. In order to accurately track the platform globally it is necessary to refine the estimation of the camera poses and 3D locations of the feature points, which is commonly done by utilising bundle adjustment; however, existing software packages providing such methods do not take the specific problem geometry into account, and the result is a physically inconsistent solution. We develop a bundle adjustment algorithm which incorporates the planar motion constraint, and devise a scheme that utilises the sparse structure of the problem. Experiments are carried out on real data and the proposed algorithm shows an improvement compared to established generic methods.
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