An Adaptive SLAM Algorithm Based on Strong Tracking UKF

Unscented Kalman filter (UKF) is lack of adaptive on-line adjustment ability that seriously decreases the estimation accuracy of system state. To deal with this problem, this paper proposes an improved SLAM (simultaneous localization and mapping) algorithm that combines the strengths of strong tracking filter (STF) and UKF. Each sampling point of UKF is updated by STF, the effects of noises on system state estimation are suppressed by optimizing filter gains, and the system state estimation converges to real values quickly. Performances of several SLAM algorithm in different noisy environments are compared by simulation. The experimental results show that this adaptive SLAM algorithm based on STF and UKF is of better adaptability and robustness.