This article presents a comprehensive study on multisensor data fusion for accurate position estimation of a mobile robot. We employ the Smooth Variable Structure Filter (SVSF), a state estimation technique known for its robustness against uncertainties and disturbances. This makes it particularly well-suited for dynamic and noisy environments where precise localization is critical. In our approach, the SVSF is applied to fuse data from multiple sensors, leveraging their complementary strengths to deliver accurate and reliable position estimates. To validate the effectiveness of this method, we conducted experiments using the KITTI Vision Benchmark dataset, a widely recognized standard for evaluating robotic perception and localization systems. The experimental results demonstrate that the SVSF achieves significantly lower Root Mean Square Error (RMSE) compared to individual sensor estimates. This underscores its ability to mitigate the impact of noise, sensor errors, and environmental uncertainties while providing a more precise position estimate. Furthermore, the study highlights the scalability and adaptability of the SVSF to various robotic applications, as it can seamlessly integrate data from diverse sensor modalities such as IMUs, GPS, cameras, and LiDAR. This flexibility positions the SVSF as a powerful tool for autonomous navigation in complex and unpredictable environments.