Virtual lifeline: Multimodal sensor data fusion for robust navigation in unknown environments

Widyawan,; Pirkl, Gerald; Munaretto, Daniele; Fischer, Carl; An, Chunlei; Lukowicz, Paul; Klepal, Martin; Timm‐Giel, Andreas; Widmer, Joerg; Pesch, Dirk; Gellersen, Hans

doi:10.1016/j.pmcj.2011.04.005

Cited by 37 publications

(23 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, similar results are also expected if using different navigation systems (e.g. other pedestrian navigation systems (Ruiz et al, 2012, Widyawan et al, 2012, Foxlin, 2005). As previously assumed the camera embedded in the device has been calibrated by using the OpenCV camera calibration toolbox (Intel Research, 2000), and hence feature point coordinates have been expressed as normalized homogeneous coordinates.…”

Section: Resultssupporting

confidence: 76%

Photogrammetric Reconstruction With Bayesian Information

Masiero¹,

Fissore²,

Guarnieri³

et al. 2016

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

View full text Add to dashboard Cite

ABSTRACT:Nowadays photogrammetry and laser scanning methods are the most wide spread surveying techniques. Laser scanning methods usually allow to obtain more accurate results with respect to photogrammetry, but their use have some issues, e.g. related to the high cost of the instrumentation and the typical need of high qualified personnel to acquire experimental data on the field. Differently, photogrammetric reconstruction can be achieved by means of low cost devices and by persons without specific training. Furthermore, the recent diffusion of smart devices (e.g. smartphones) embedded with imaging and positioning sensors (i.e. standard camera, GNSS receiver, inertial measurement unit) is opening the possibility of integrating more information in the photogrammetric reconstruction procedure, in order to increase its computational efficiency, its robustness and accuracy. In accordance with the above observations, this paper examines and validates new possibilities for the integration of information provided by the inertial measurement unit (IMU) into the photogrammetric reconstruction procedure, and, to be more specific, into the procedure for solving the feature matching and the bundle adjustment problems.

show abstract

Section: Resultssupporting

confidence: 76%

Photogrammetric Reconstruction With Bayesian Information

Masiero¹,

Fissore²,

Guarnieri³

et al. 2016

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

View full text Add to dashboard Cite

show abstract

“…Given the high request for accurate metric reconstructions, positioning in such working conditions is typically obtained by integrating information provided by different sensors (e,g, inertial measurements provided by the Inertial Measurement Unit (IMU), acceleration, gyroscope and magnetometer measurements, vision, and WiFi, if available (Saeedi et al, 2014, Widyawan et al, 2012). A recent example of mobile mapping system allowing accurate surveying also in indoor environments is the Leica Pegasus backpack.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Low Cost Photogrammetric Survey With TLS And Leica Pegasus Backpack 3d Modelss

Masiero

Fissore

Guarnieri

et al. 2017

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

View full text Add to dashboard Cite

ABSTRACT:This paper considers Leica backpack and photogrammetric surveys of a mediaeval bastion in Padua, Italy. Furhtermore, terrestrial laser scanning (TLS) survey is considered in order to provide a state of the art reconstruction of the bastion. Despite control points are typically used to avoid deformations in photogrammetric surveys and ensure correct scaling of the reconstruction, in this paper a different approach is considered: this work is part of a project aiming at the development of a system exploiting ultra-wide band (UWB) devices to provide correct scaling of the reconstruction. In particular, low cost Pozyx UWB devices are used to estimate camera positions during image acquisitions. Then, in order to obtain a metric reconstruction, scale factor in the photogrammetric survey is estimated by comparing camera positions obtained from UWB measurements with those obtained from photogrammetric reconstruction. Compared with the TLS survey, the considered photogrammetric model of the bastion results in a RMSE of 21.9cm, average error 13.4cm, and standard deviation 13.5cm. Excluding the final part of the bastion left wing, where the presence of several poles make reconstruction more difficult, (RMSE) fitting error is 17.3cm, average error 11.5cm, and standard deviation 9.5cm. Instead, comparison of Leica backpack and TLS surveys leads to an average error of 4.7cm and standard deviation 0.6cm (4.2cm and 0.3cm, respectively, by excluding the final part of the left wing).

show abstract

“…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%

Indoor Photogrammetry Aided With Uwb Navigation

Masiero

Fissore

Guarnieri

et al. 2018

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

View full text Add to dashboard Cite

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.

show abstract

Virtual lifeline: Multimodal sensor data fusion for robust navigation in unknown environments

Cited by 37 publications

References 18 publications

Photogrammetric Reconstruction With Bayesian Information

Photogrammetric Reconstruction With Bayesian Information

Comparison of Low Cost Photogrammetric Survey With TLS And Leica Pegasus Backpack 3d Modelss

Indoor Photogrammetry Aided With Uwb Navigation

Contact Info

Product

Resources

About