Unscented SLAM with conditional iterations

As reported, the extended Kalman filter based simultaneous localization and mapping (SLAM) algorithm has two serious drawbacks, namely the linear approximation of non-linear functions and the calculation of Jacobian matrices. These can introduce estimation error and induce a great ambiguity for data association. For overcoming these drawbacks, this paper presents an improved SLAM solution, based on the unscented Kalman filter (UKF) with conditional iterations (UiSLAM). Since the UKF can improve the performance of filters, it can be used to overcome the drawbacks of the previous frameworks. When the loop is closed, the condition to perform iterated update is satisfied. Then the iterative update procedure employed in the iterated extended Kalman filter (IEKF) is implemented. This approach combines the virtues of IEKF and UKF for solving the SLAM problems and improves accuracy of the state estimation. Both the simulation and experimental results are proposed to illustrate the superiority of the UiSLAM algorithm over previous approaches.