Abstract
In our previous work on visual, appearance-based localization and mapping, we presented in [14] a novel SLAM approach to build visually labeled topological maps. The essential contribution of this work was an adaptive sensor model, which is estimated online, and a graph matching scheme to evaluate the likelihood of a given topological map. Both methods enable the combination of an appearance-based, visual localization and mapping concept with a Rao-Blackwellized Particle Filter (RBPF) as state estimator to a real-world suitable, online SLAM approach. In this paper we improve our algorithm by using a novel probability driven approximation of the local similarity function (the sensor model) to deal with dynamic changes of the appearance in the operation area.1
The research leading to these results has received funding from the European Community’s Seventh Framework Programme ([FP7/2007-2013] [FP7/2007–2011]) under grant agreement n∘ 216487 (CompanionAble: http://www.companionable.net/)
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Keßler, J., König, A., Gross, HM. (2009). An Improved Sensor Model on Appearance Based SLAM. In: Dillmann, R., Beyerer, J., Stiller, C., Zöllner, J.M., Gindele, T. (eds) Autonome Mobile Systeme 2009. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10284-4_20
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DOI: https://doi.org/10.1007/978-3-642-10284-4_20
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