Title :
The Resection Filter: An Alternative Approach to Filtering Gaussian Pose Estimates with Bearing Observations
Author :
Easton, Adam ; Cameron, Stephen
Author_Institution :
Comput. Lab., Oxford Univ.
Abstract :
This paper presents the resection filter, which is a new filter for fusing bearings to known landmarks with a prior Gaussian pose estimate. The filter calculates a robot´s pose based on noisy bearings independently from any prior pose estimate and then fuses this value with a prior pose estimate. Implementations of the extended Kalman filter and unscented Kalman filter are described and several experiments are conducted comparing the proposed filter with these two benchmarks. The results show that the new filter outperforms both benchmarks in nearly all the areas we considered, at least for problems in which bearing information is readily available
Keywords :
Gaussian processes; Kalman filters; direction-of-arrival estimation; nonlinear filters; pose estimation; robots; Gaussian pose estimates; bearing observations; extended Kalman filter; resection filter; robots; unscented Kalman filter; Algorithm design and analysis; Cameras; Fuses; Information filtering; Information filters; Intelligent robots; Iterative methods; Laboratories; Nonlinear filters; Probability distribution;
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0258-1
Electronic_ISBN :
1-4244-0259-X
DOI :
10.1109/IROS.2006.282391