An RFS theoretic for Bayesian feature-based robotic mapping
MetadataShow full item record
Estimating a FB map requires the joint propagation of the FB map density encapsulating uncertainty in feature number and location. This chapter addresses the joint propagation of the FB map density and leads to an optimal map estimate in the presence of unknown map size, spurious measurements, feature detection and data association uncertainty. The proposed framework further allows for the joint treatment of error in feature number and location estimates. As a proof of concept, the first-order moment recursion, the PHD filter, is implemented using both simulated and real experimental data. The feasibility of the proposed framework is demonstrated, particularly in situations of high clutter density and large data association ambiguity. This chapter establishes new tools for a more generalised representation of the FB map, which is a fundamental component of the more challenging SLAM problem, to follow in Part II.
Showing items related by title, author, creator and subject.
Horn, Z.; Auret, L.; McCoy, J.; Aldrich, Chris; Herbst, B. (2017)Â© 2017 Image-based soft sensors are of interest in process industries due to their cost-effective and non-intrusive properties. Unlike most multivariate inputs, images are highly dimensional, requiring the use of feature ...
Aizam, Nur Aidya Hanum (2013)Timetabling is a table of information showing when certain events are scheduled to take place. Timetabling is in fact very essential in making sure that all events occur in the time and place required. It is critical in ...
Tran, The Truyen; Luo, W.; Phung, D.; Gupta, S.; Rana, S.; Kennedy, R.; Larkins, A.; Venkatesh, S. (2014)Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity ...