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.
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