This paper introduces a probabilistic grid-based approach for robot mapping in dynamic environments, using cell-specific Hidden Markov Models to represent occupancy and its changes. The method learns state transition probabilities from observed data, enabling online adaptation and prediction of future occupancy states, improving map accuracy and path planning compared to traditional occupancy grids, particularly in semi-static environments like parking lots.

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This paper presents a novel approach to robot mapping in changing environments using Hidden Markov Models, which is a significant contribution. The experimental results demonstrate the effectiveness of the method, and the online parameter estimation approach is particularly valuable for real-world applications. While the evaluation could be extended to more complex and dynamic scenarios, the paper's methodology and findings are solid.