Abstract
Accurately detecting changes in one’s environment is an important ability for many application domains, but can be challenging for humans. Autonomous robots can easily be made to autonomously detect metric changes in the environment, but unlike humans, understanding context can be challenging for robots. We present a novel system that uses an autonomous robot performing point cloud-based change detection to facilitate information-gathering tasks and provides enhanced situational awareness. The robotic system communicates detected changes via augmented reality to a human teammate for evaluation. We present results from a fielded system using two differently-equipped robots to examine implementation questions of point cloud density and its effect on visualization of changes. Our results show that there are trade-offs between implementations that we believe will be constructive towards similar systems in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dellaert, F.: Factor graphs and GTSAM: a hands-on introduction. Technical report, Georgia Institute of Technology (2012)
Dellaert, F., Kaess, M.: Square Root SAM: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)
Durlach, P.J.: Change blindness and its implications for complex monitoring and control systems design and operator training. Hum.-Comput. Interact. 19(4), 423–451 (2004)
Gregory, J., et al.: Application of multi-robot systems to disaster-relief scenarios with limited communication. In: Wettergreen, D.S., Barfoot, T.D. (eds.) Field and Service Robotics. STAR, vol. 113, pp. 639–653. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27702-8_42
Marsland, S., Nehmzow, U., Shapiro, J.: On-line novelty detection for autonomous mobile robots. Robot. Auton. Syst. 51(2–3), 191–206 (2005)
Núñez, P., Drews, P., Rocha, R., Campos, M., Dias, J.: Novelty detection and 3D shape retrieval based on Gaussian mixture models for autonomous surveillance robotics. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4724–4730. IEEE (2009)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99, 215–249 (2014)
Quigley, M., Faust, J., Foote, T., Leibs, J.: ROS: an open-source Robot Operating System. In: ICRA Workshop on Open Source Software (2009)
Reardon, C., Lee, K., Fink, J.: Come see this! Augmented reality to enable human-robot cooperative search. In: Proceedings of the 2018 IEEE Symposium on Safety, Security, and Rescue Robotics (2018)
Reardon, C., Lee, K., Rogers, J.G., Fink, J.: Augmented reality for human-robot teaming in field environments. In: Chen, J.Y.C., Fragomeni, G. (eds.) HCII 2019. LNCS, vol. 11575, pp. 79–92. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21565-1_6
Rusu, R.B., Cousins, S.: 3D is here: point cloud library PCL. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4. IEEE (2011)
Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics: Science and Systems, vol. 2, p. 435 (2009)
Simons, D.J., Chabris, C.F.: Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception 28(9), 1059–1074 (1999)
Sofman, B., Neuman, B., Stentz, A., Bagnell, J.A.: Anytime online novelty and change detection for mobile robots. J. Field Robot. 28(4), 589–618 (2011)
Sturari, M., Paolanti, M., Frontoni, E., Mancini, A., Zingaretti, P.: Robotic platform for deep change detection for rail safety and security. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2017)
Szafir, D.: Mediating human-robot interactions with virtual, augmented, and mixed reality. In: Chen, J.Y.C., Fragomeni, G. (eds.) HCII 2019. LNCS, vol. 11575, pp. 124–149. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21565-1_9
Trevor, A.J.B., Rogers, J.G., Christensen, H.I.: OmniMapper: a modular multimodal mapping framework. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1983–1990, May 2014. https://doi.org/10.1109/ICRA.2014.6907122
Vieira, A.W., Drews, P.L., Campos, M.F.: Spatial density patterns for efficient change detection in 3D environment for autonomous surveillance robots. IEEE Trans. Autom. Sci. Eng. 11(3), 766–774 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Reardon, C., Gregory, J., Nieto-Granda, C., Rogers, J.G. (2020). Enabling Situational Awareness via Augmented Reality of Autonomous Robot-Based Environmental Change Detection. In: Chen, J.Y.C., Fragomeni, G. (eds) Virtual, Augmented and Mixed Reality. Design and Interaction. HCII 2020. Lecture Notes in Computer Science(), vol 12190. Springer, Cham. https://doi.org/10.1007/978-3-030-49695-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-49695-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49694-4
Online ISBN: 978-3-030-49695-1
eBook Packages: Computer ScienceComputer Science (R0)