CAJ | 학술논문

针对扩展卡尔曼滤波( EKF)算法在移动机器人同时定位和环境建模( SLAM)中的缺点，即非线性系统简单线性化所导致的系统状态方程的不准确性、雅克比矩阵的计算所导致的计算复杂化以及噪声模型不确定性所导致的滤波稳定性降低等问题，提出一种对噪声自适应的UKF-SLAM算法。该算法通过对噪声缩放进而改变噪声模型，利用观测残差序列准确估计观测噪声模型协方差，运用预测的新息协方差和IAE开窗法求其系统状态噪声缩放因子，从而准确估计系统状态噪声模型协方差，实现对不确定的噪声模型能够自适应UKF-SLAM算法。 UKF的Sigma点采样策略是比例对称采样。实验结果证明，该方法相对EKF算法和UKF算法具有较高的定位精度和自适应能力。
침대확전잡이만려파( EKF)산법재이동궤기인동시정위화배경건모( SLAM)중적결점，즉비선성계통간단선성화소도치적계통상태방정적불준학성、아극비구진적계산소도치적계산복잡화이급조성모형불학정성소도치적려파은정성강저등문제，제출일충대조성자괄응적UKF-SLAM산법。해산법통과대조성축방진이개변조성모형，이용관측잔차서렬준학고계관측조성모형협방차，운용예측적신식협방차화IAE개창법구기계통상태조성축방인자，종이준학고계계통상태조성모형협방차，실현대불학정적조성모형능구자괄응UKF-SLAM산법。 UKF적Sigma점채양책략시비례대칭채양。실험결과증명，해방법상대EKF산법화UKF산법구유교고적정위정도화자괄응능력。
For Extend Kalman Filtering ( EKF ) algorithm disadvantage on the Simultaneous Location and Mapping ( SLAM) ,that is simple linearization of nonlinear systems resulting from the inaccuracy of the system state equation,Jacobi matrix calculation resulting from computational complexity,and noise uncertainty caused by the filtering reduced stability and other issues, this paper proposes a noise adaptive UKF-SLAM algorithm. In order to achieve adaptive UKF-SLAM algorithm,the paper scales the noise to change the noise model. Using the observed innovation sequence to accurately estimate the covariance of the measurement noise model. And using a new message convariance and IAE fenestration to find the system noise scaling factor,and thus accurately setimates the convariance of the system state noise model,it achieves a adaptive UKF-SLAM algorithm. The sampling strategy of UKF Sigma points is scaling symmetric sampling. Experimental results show that the algorithm has a high accuracy on SLAM compared with the EKF-SLAM and UKF-SLAM.