Hand Waving Away Scale

Ham, Christopher C. W.; Lucey, Simon; Singh, Surya P. N.

doi:10.1007/978-3-319-10593-2_19

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…The closest previous works to ours are the papers by Ham et al [25], [26] which address the same problem in a similar context. That is, they also apply off-the-shelf visual tracking software to recover the camera poses up to scale and thereafter fix the metric scale based on inertial measurements.…”

Section: Related Workmentioning

confidence: 74%

Inertial-based scale estimation for structure from motion on mobile devices

Mustaniemi

Kannala

Särkkä

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Structure from motion algorithms have an inherent limitation that the reconstruction can only be determined up to the unknown scale factor. Modern mobile devices are equipped with an inertial measurement unit (IMU), which can be used for estimating the scale of the reconstruction. We propose a method that recovers the metric scale given inertial measurements and camera poses. In the process, we also perform a temporal and spatial alignment of the camera and the IMU. Therefore, our solution can be easily combined with any existing visual reconstruction software. The method can cope with noisy camera pose estimates, typically caused by motion blur or rolling shutter artifacts, via utilizing a Rauch-TungStriebel (RTS) smoother. Furthermore, the scale estimation is performed in the frequency domain, which provides more robustness to inaccurate sensor time stamps and noisy IMU samples than the previously used time domain representation. In contrast to previous methods, our approach has no parameters that need to be tuned for achieving a good performance. In the experiments, we show that the algorithm outperforms the state-of-the-art in both accuracy and convergence speed of the scale estimate. The accuracy of the scale is around 1% from the ground truth depending on the recording. We also demonstrate that our method can improve the scale accuracy of the Project Tango's build-in motion tracking.

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Section: Related Workmentioning

confidence: 74%

Inertial-based scale estimation for structure from motion on mobile devices

Mustaniemi

Kannala

Särkkä

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…smoothing) and to the integration of other sensor information in the positioning algorithm (e.g. inertial sensors, WiFi signals (Ham et al, 2014, Widyawan et al, 2012, Saeedi et al, 2014, Masiero et al, 2014 Future investigation foresees also the use of different techniques for estimating model orientation (e.g. with respect to North-East directions) (Alsubaie et al, 2017), and more advanced inertial sensor error modeling (Radi et al, 2018) 6.…”

Section: Discussionmentioning

confidence: 99%

“…Despite this procedure is widely used and it is well known to ensure reliable and accurate results, the use of GNSS and control points can be difficult in certain operating conditions, e.g. when surveying areas difficult to reach for humans/terrestrial vehicles (Bendea et al, 2008), in indoor environments (Tucci et al, 2018, Dabove et al, 2018 (recently scale estimation has been investigated also for smartphone-based indoor photogrammetric reconstructions, typically exploiting inertial sensor information (Mustaniemi et al, 2017, Ham et al, 2014, Alsubaie et al, 2017). Thanks to the need of providing 3D models also in such conditions, 3D modelling without the need of control points (e.g.…”

Section: Introductionmentioning

confidence: 99%

Indoor Photogrammetry Aided With Uwb Navigation

Masiero

Fissore

Guarnieri

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The subject of photogrammetric surveying with mobile devices, in particular smartphones, is becoming of significant interest in the research community. Nowadays, the process of providing 3D point clouds with photogrammetric procedures is well known. However, external information is still typically needed in order to move from the point cloud obtained from images to a 3D metric reconstruction. This paper investigates the integration of information provided by an UWB positioning system with visual based reconstruction to produce a metric reconstruction. Furthermore, the orientation (with respect to North-East directions) of the obtained model is assessed thanks to the use of inertial sensors included in the considered UWB devices. Results of this integration are shown on two case studies in indoor environments.

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“…Due to the low-cost design, most devices do not synchronize the IMU and image data using, e.g., a global common timestamp. In [20] it is shown that this may be critical when designing methods that fuse IMU and image data. Our preliminary experiments demonstrated that for our device the IMU data could be not synchronized w.r.t.…”

Section: Data Collection Methodologymentioning

confidence: 98%

“…A known issue affecting mobile devices is the synchronization between the data provided by the IMU sensors and the image provided by the camera [20]. Due to the low-cost design, most devices do not synchronize the IMU and image data using, e.g., a global common timestamp.…”

Section: Data Collection Methodologymentioning

confidence: 99%

The toulouse vanishing points dataset

Angladon

Gasparini

Charvillat

2015

Proceedings of the 6th ACM Multimedia Systems Conference

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In this paper we present the Toulouse Vanishing Points Dataset, a public photographs database of Manhattan scenes taken with an iPad Air 1 . The purpose of this dataset is the evaluation of vanishing points estimation algorithms. Its originality is the addition of Inertial Measurement Unit (IMU) data synchronized with the camera under the form of rotation matrices. Moreover, contrary to existing works which provide vanishing points of reference in the form of single points, we computed uncertainty regions. The Toulouse Vanishing Points Dataset is publicly available at

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Hand Waving Away Scale

Cited by 12 publications

References 10 publications

Inertial-based scale estimation for structure from motion on mobile devices

Inertial-based scale estimation for structure from motion on mobile devices

Indoor Photogrammetry Aided With Uwb Navigation

The toulouse vanishing points dataset

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