Abstract:
A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization
and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled
here for cases when only bearing measurements are available. Within the stochastic mapping
framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf)
is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a
sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range
uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In
our approach, the Gaussian parameters are designed on the basis of minimizing the
representation error. The computational complexity of the GSF is reduced by applying the
sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive
experimental results are included to demonstrate the effectiveness and efficiency of the proposed
techniques.
