Abstract
The Virtual Autonomous Navigation Environment (VANE) is a high-fidelity simulation environment for ground robotics. Physics-based realism is the primary goal of the VANE. The VANE simulation incorporates realistic lighting, vehicle-terrain interaction, environmental attributions, and sensors. The sensor models, including camera, laser ranging, and GPS, are the focus of this work. These sensor models were designed to incorporate both internal (electronic) and external (environment) noise in order to produce a sensor output that closely matches that produced in real-world environments. This sensor output will allow roboticists to use simulation further into the development and debugging process before exposing robots to field conditions.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Craighead, J., Murphy, R., Burke, J., Goldiez, B.: A survey of commercial & open source unmanned vehicle simulators. In: 2007 IEEE International Conference on Robotics and Automation, pp. 852–857 (2007)
Balakirsky, S., Scrapper, C., Carpin, S., Lewis, M.: UsarSim: providing a framework for multirobot performance evaluation. In: Proceedings of PerMIS, Citeseer, vol. 2006 (2006)
Goodin, C., Kala, R., Carrrillo, A., Liu, L.Y.: Sensor modeling for the virtual autonomous navigation environment. IEEE Sensors (2009)
Subbarao, M., Nikzad, A.: Model for image sensing and digitization in machine vision. In: Proceedings of SPIE, vol. 1385, p. 70. SPIE, San Jose (1991)
Born, M., Wolf, E.: Principles of Optics, 6th edn. Pergamon Press, Elmsford (1980)
Tuley, J., Vandapel, N., Hebert, M.: Analysis and Removal of artifacts in 3-D LADAR Data. In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 2203–2210 (2005)
Rong, L., Chen, W., Yu, S., Song, W.: Mathematical model of coordinate transformations for 3D Depth-of-field collection system. In: 6th IEEE International Conference on Industrial Informatics, INDIN 2008, pp. 80–85 (2008)
SOPAC: Scripps orbit and permanent array center (2010), http://sopac.ucsd.edu/cgi-bin/dbShowArraySitesMap.cgi
Mehrtash, M.: Gps navigation toolbox gnt08.1.2 GNT08.1.2 (2008), http://www.mathworks.com/matlabcentral/fileexchange/20578i
RINEX: Receiver independent exchange format, ver. 2.10 (2009), http://gps.wva.net/html.common/rinex.html
Montenbruck, O., Garcia-Fernandez, M.: Ionosphere Path Delay Models for Spaceborne GPS. Deutsches Zentrum für Luft- und Raumfahrt DLR-GSOC TN 05-07 (2005)
Niell, A.E.: Improved atomspheric mapping functions for VLBI and GPS. Earth Planets Space 52, 699–702 (2000)
Niell, A.: The IMF Mapping Functions. In: GPSMet Workshop (2003)
Kim, H.I., Ha, J., Park, K.D., Lee, S., Kim, J.: Comparison of Tropospheric Signal Delay Models for GNSS Error Simulation. Journal of Astronomy and Space Sciences 26, 21–220 (2000)
Hwang, C., Hwang, L.S.: Satellite orbit error due to geopotential model error using perturbation theory: applications to ROCSAT-2 and COSMIC missions. Computers and Geosciences 28, 357–367 (2000)
Parkinson, B., Spilker, J.: The global positioning system: theory and applications. In: Aiaa (1996)
Rohde, M., Crawford, J., Toschlog, M., Iagnemma, K., Kewlani, G., Cummins, C., Jones, R., Horner, D.: An interactive physics-based unmanned ground vehicle simulator leveraging open source gaming technology: progress in the development and application of the virtual autonomous navigation environment (VANE) desktop. In: Gerhart, G.R., Gage, D.W., Shoemaker, C.M. (eds.) Unmanned Systems Technology XI. SPIE, Orlando (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goodin, C., Durst, P.J., Gates, B., Cummins, C., Priddy, J. (2010). High Fidelity Sensor Simulations for the Virtual Autonomous Navigation Environment. In: Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., von Stryk, O. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2010. Lecture Notes in Computer Science(), vol 6472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17319-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-17319-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17318-9
Online ISBN: 978-3-642-17319-6
eBook Packages: Computer ScienceComputer Science (R0)