To address the challenge of accurately determining the environmental region of unmanned vehicles during seamless indoor-outdoor positioning, a regional recognition method for seamless indoor-outdoor localization is proposed. Firstly, a joint prediction model integrating particle swarm optimization-support vector machine (PSO-SVM) and hidden Markov model (HMM) is designed. Environmental feature data acquired by sensors serve as model inputs to generate regional recognition results. Secondly, three environmental models are introduced to describe the vehicle's operational environment, with corresponding measurement information selected based on the regional recognition outcomes. Finally, the regional transition probabilities are utilized to update the switching probabilities of the three environmental sub-models in the interactive multiple model (IMM) algorithm, thereby enhancing the accuracy of environmental region recognition and positioning precision for seamless indoor-outdoor navigation. The results of real-vehicle experiment show that the proposed joint recognition method achieves an accuracy of 98.09% in region recognition, representing improvements of 2.13% and 9.53% compared to using PSO-SVM or HMM alone. Further experiments indicate that the proposed seamless positioning method enhances the average positioning accuracy by 43.75% and 22.30% compared to the traditional federated Kalman filter (FKF) algorithm and IMM algorithm, respectively.