Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sampling and noisy sampling, a case present in many real-world problems in the field of traffic sensing and environment monitoring, etc. However, jointly exploiting the spatiotemporal and periodic structure, which is generally not captured by classical matrix completion approaches, can improve the imputation performance of sensor data in such real-world conditions.We present a Bayesian approach toward spatiotemporal matrix completion wherein we estimate the underlying temporarily varying subspace using a Variational Bayesian technique. We jointly couple the low-rank matrix completion with the state space autoregressive framework along with a penalty function on the slowly varying subspace to model the temporal and periodic evolution in the data. We also propose a robust version of the above formulation, which improves the performance of imputation in the presence of outliers. Results demonstrate that the proposed method outperforms the recent state-of-the-art methods for real-world traffic and air pollution data. We demonstrate that fusing the subspace evolution over days can improve the imputation performance with even 15% of the data sampling.