Computational complexity reduced CDKF based SLAM algorithm
计算复杂度降低的基于CDKF的SLAM算法

In order to reduce the computational complexity of the CDKF SLAM algorithm for large-scale environment, this paper proposed an improved CDKF SLAM algorithm which was presented in the context of the linear-regression Kalman filter. Based on the properties of SLAM, it improved the sampling strategy by reconstructing the estimated state and its covariance during prediction and measurement update. The complexity was thus reduced to On2. Simulation experiments in different scale environments and experiments of the car park database proves that the proposed algorithm keep the same accuracy as the general CDKF SLAM, while its running time became much shorter. This makes it more suitable for applications in large-scale environment.