This work attempts to answer two problems. (1) Can we use the odometry information from two different Simultaneous Localization And Mapping (SLAM) algorithms to get a better estimate of the odometry? and (2) What if one of the SLAM algorithms gets affected by shot noise or by attack vectors, and can we resolve this situation? To answer the first question we focus on fusing odometries from Lidar-based SLAM and Visual-based SLAM using the Extended Kalman Filter (EKF) algorithm. The second question is answered by introducing the Maximum Correntropy Criterion - Extended Kalman Filter (MCC-EKF), which assists in removing/minimizing shot noise or attack vectors injected into the system. We manually simulate the shot noise and see how our system responds to the noise vectors. We also evaluate our approach on KITTI dataset for self-driving cars.