Long exposure localization in darkness using consumer cameras
Milford, Michael, Turner, Ian, & Corke, Peter (2013) Long exposure localization in darkness using consumer cameras. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, IEEE, Kongresszentrum Karlsruhe, Karlsruhe, Germany.
In this paper we demonstrate passive vision-based localization in environments more than two orders of magnitude darker than the current benchmark using a 100 webcam and a 500 camera. Our approach uses the camera’s maximum exposure duration and sensor gain to achieve appropriately exposed images even in unlit night-time environments, albeit with extreme levels of motion blur. Using the SeqSLAM algorithm, we first evaluate the effect of variable motion blur caused by simulated exposures of 132 ms to 10000 ms duration on localization performance. We then use actual long exposure camera datasets to demonstrate day-night localization in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed greyscale images to using patch normalization and local neighbourhood normalization – the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function across extreme perceptual change.
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|Item Type:||Conference Paper|
|Keywords:||Localization, Visual navigation, Recognition|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2013 IEEE|
|Deposited On:||21 May 2013 22:39|
|Last Modified:||12 Sep 2016 01:24|
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