Experimental Comparison of Bounded-Error State Estimation and Constraints Propagation
The vehicle’s localization is classically achieved by Bayesian methods like Extended Kalman Filtering. Such methods provide an estimated position with its associated uncertainty. Bounded-error approaches (Bounded-Error State Estimation and Constraints Propagation) use interval analysis and work in a different way as they provide a possible set of positions. An advantage of bounded-error approaches over Bayesian methods is that their results are guaranteed (whereas the results of Bayesian methods are probabilistically defined). This paper compares both Bounded-Error State Estimation and Constraints Propagation using the same experimental data. The results obtained aim to rank these approaches in terms of computing time, consistency and imprecision.
Bastien Vincke Alain Lambert
IEF,UMR CNRS 8622,Université Paris Sud,Bat. 220,Centre dOrsay,91405 Orsay cedex - France
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4724-4729
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）