Skip to main content
Log in

Human-Like Sensing for Robotic Remote Inspection and Analytics

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper we introduce Human-like Sensing or “5 senses computing” as a natural futuristic extension of Internet-of-Things. We discuss how 3D optical vision, thermal vision, acoustic profiling, olfaction and tactile sensing can help in remote inspection and analytics solutions. We propose a robot mounted opto-thermal and acoustic sensing system as a possible integrated system to gather such data. We present results of field experiments conducted with the proposed system and show how such systems can provide acceptable solutions for remote inspection and analytics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://readwrite.com/2012/12/18/ibms-cognitive-computing-plans-giving-smartphones-5-senses.

  2. http://www.industrialvision.co.uk/vision-systems.

  3. http://www.microscan.com/en-us/Technology/MachineVisionSystems/machine-vision.aspx.

  4. http://www.laserfocusworld.com/articles/print/volume-46/issue-1/features/optical-surface-profiling.html.

  5. http://www.flir.com.hk/flirone/.

References

  1. Shirmohammadi, S., & Ferrero, A. (2014). Camera as the instrument: The rising trend of vision based measurement. Instrumentation & Measurement Magazine, IEEE, 17(3), 41–47.

    Article  Google Scholar 

  2. Kundu, T. (2014). Acoustic source localization. Ultrasonics, 54(1), 25–38.

    Article  Google Scholar 

  3. Ibarra-Castanedo, C., Sfarra, S., Genest, M., & Maldague, X. (2015). Infrared vision: Visual inspection beyond the visible spectrum. In Integrated imaging and vision techniques for industrial inspection (pp. 41–58). London: Springer.

  4. Araki, H., & Omatu, S. (2015). A proposition and an evaluation for compact multiple odor scan system. In Control conference (ASCC), 2015 10th Asian (pp. 1–6). IEEE.

  5. Ha, D., Sun, Q., Kaiqi, S., Wan, H., Li, H., Xu, N., et al. (2015). Recent achievements in electronic tongue and bioelectronic tongue as taste sensors. Sensors and Actuators B: Chemical, 207, 1136–1146.

    Article  Google Scholar 

  6. Noor, A. K. (2015). Potential of cognitive computing and cognitive systems. Open Engineering, 5(1), 75–88.

    Google Scholar 

  7. Pradeep, V., Rhemann, C., Izadi, S., Zach, C., Bleyer, M., & Bathiche, S. (2013). MonoFusion: Real-time 3D reconstruction of small scenes with a single web camera. In The 13th IEEE international symposium on mixed and augmented reality (pp. 83–88).

  8. Pizzoli, M., Forster, C., & Scaramuzza, D. (2014). REMODE: Probabilistic, monocular dense reconstruction in real time. In IEEE international conference on robotics and automation (ICRA), Hong Kong (pp. 2609–2616).

  9. Pollefeys, M., Nister, D., Frahm, D. J. M., Akbarzadeh, A., Mordohai, P., Clipp, B., et al. (2008). Detailed real-time urban 3D reconstruction from video. International Journal of Computer Vision, 78(2–3), 143–167.

    Article  Google Scholar 

  10. Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011). DTAM: Dense tracking and mapping in real-time. In ICCV (pp. 2320–2327).

  11. Saha, A., Bhowmick, B., & Sinha, A. (2014). A system for near real-time 3D reconstruction from multi-view using 4G enabled mobile. In Proceedings of the 2014 IEEE international conference on mobile services. IEEE Computer Society.

  12. Tanskanen, P., Kolev*, K., Meier, L., Paulsen, F. C., Saurer, O., & Pollefeys, M. (2013). Live metric 3D reconstruction on mobile phones. In ICCV (pp. 65–72).

  13. Bhowmick, B., Mallik, A., & Saha, A. (2014). Mobiscan3D: A low cost framework for real time dense 3D reconstruction on mobile devices. In IEEE 11th international conference on Ubiquitous intelligence and computing (pp. 783–788).

  14. Mallik, A., Bhowmick, B., & Alam, S. (2015). A multi-sensor information fusion approach for efficient 3D reconstruction in smart phone. In International conference on image processing, computer vision, and pattern recognition (IPCV) (pp. 291–298).

  15. Davison, A. (2003). Real-time simultaneous localisation and mapping with a single camera. In IEEE international conference on computer vision (pp. 1403–1410).

  16. Gonzalez, R. C., Valdés, R., & Cancelas, J. A. (2001). Vision based measurement system to quantify straightness defect in steel sheets. In 9th International conference on computer analysis of images and patterns (pp. 427–4341). Berlin: Springer, ISBN 3-540-42513-6.

  17. Mottaa, J. M. S. T., Carvalhob, G. C., & McMasterc, R. S. (2001). Robot calibration using a 3D vision-based measurement system with a single camera. doi:10.1016/S0736-5845(01)00024-2.

  18. Shirmohammadi, S., & Ferrero, A. (2014). Camera as the instrument: The rising trend of vision based measurement. Instrumentation & Measurement Magazine, IEEE, 17(3), 41–47.

    Article  Google Scholar 

  19. Rövid, A. (2013). Machine vision-based measurement system for vehicle body inspection. Acta Polytechnica Hungarica. doi:10.12700/APH.10.05.2013.5.9.

    Google Scholar 

  20. Bhanu, B., & Holben, R. D. (1990). Model-based segmentation of FLIR images. Aerospace and Electronic Systems, IEEE Transactions on, 26(1), 2–11.

    Article  Google Scholar 

  21. Borrmann, D., Nuchter, A., Dakulovic, M., Maurovic, I., Petrovic, I., Osmankovic, D., & Velagic, J. (2012). The project ThermalMapper thermal 3D mapping of indoor environments for saving energy. In Proceedings of the 10th international IFAC symposium on robot control (SYROCO) (Vol. 10).

  22. Vidas, S., Moghadam, P., & Bosse, M. (2013). 3D thermal mapping of building interiors using an RGB-D and thermal camera. In Proceedings of the IEEE international conference on robotics and automation.

  23. https://dev.windows.com/en-us/kinect. Accessed on November 26, 2015.

  24. Meilland, M., & Comport, A. (2013). Super-resolution 3D tracking and mapping. In IEEE international conference on robotics and automation.

  25. Whelan, T., Johannsson, H., Kaess, M., Leonard, J., & McDonald, J. (2013). Robust real-time visual odometry for dense RGB-D mapping. In IEEE international conference on robotics and automation (ICRA).

  26. Newcombe, R. A., Davison, A. J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., & Fitzgibbon, A. (2011). KinectFusion: Real-time dense surface mapping and tracking. In Proceedings of the ISMAR.

  27. Deshpande, P., Reddy, V. R., Saha, A., Vaiapury, K., Dewangan, K., & Dasgupta, R. (2015). A next generation mobile robot with multi-mode sense of 3D perception. In Advanced robotics (ICAR), 2015 international conference on (pp. 382–387).

  28. Vempada, R., Deshpande, P., Vaiapury, K., Saha, A., Dewangan, K., Das Gupta, R., & Pal, A. (2015). Sound source localization with 3D optical fusion for hazardous area surveillance using autonomous ground vehicles. In Conference: ICRA 2015, at Seattle USA.

  29. Lilienthal, A. J. et al. (2009). A statistical approach to gas distribution modeling with mobile robots—the Kernel DM + V algorithm. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 570–576).

  30. Firebird VI. http://www.nex-robotics.com/fire-bird-vi-robot-platform.html. Access date October 20, 2015.

  31. Martinez, A., & Fernández, E. (2013). Learning ROS for robotics programming. Packt Publishing Ltd., ISBN 978-1-78216-144-8.

  32. Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge, MA: Cambridge University Press, ISBN 0-521-54051-8.

  33. Dewangan, K., Saha, A., Vaiapury, K., & Dasgupta, R. (2015). 3D environment reconstruction using mobile robot platform & monocular vision. In Proceedings of international conference on advanced computing and communication technologies, Panipat, India.

  34. Saha, A., Dewangan, K., Dasgupta, R. (2016). 3D thermal monitoring and measurement using smart-phone and IR thermal sensor. In 11th joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP) (Vol. 3, pp. 696–702). ISBN 978-989-758-175-5.

  35. Reddy, V. R., Deshpande, P., & Dasgupta, R. (2015). Robotics audition using Kinect. In Automation, robotics and applications (ICARA), 2015 6th international conference on (pp. 34–41).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arpan Pal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pal, A., Dasgupta, R., Saha, A. et al. Human-Like Sensing for Robotic Remote Inspection and Analytics. Wireless Pers Commun 88, 23–38 (2016). https://doi.org/10.1007/s11277-016-3239-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3239-3

Keywords

Navigation