Continuous occupancy mapping methods that learn spatial representations from sensor observations support predictions at arbitrary locations, but training a single global model can become computationally expensive as the amount of training data grows during large-scale mapping. Constructing local continuous occupancy submaps reduces the size of individual learning problems, but relative pose errors can cause inconsistent predictions in overlapping regions. We address these challenges by formulating large-scale continuous occupancy mapping as a submap joining problem. Each local submap is represented by a relevance vector machine regressing a continuous average log-odds field. Repeated observations within each cell are summarized by their average log-odds and observation count, yielding a Gaussian likelihood with count-dependent precision and enabling Gaussian-conjugate inference. The resulting local fields provide predictive means, variances, and analytical spatial derivatives. We then formulate submap joining as a joint pose--field optimization problem, with consistency constraints weighted by the predictive uncertainty of overlapping local submaps. With uncertainty weights held fixed within each Gauss--Newton iteration, our joint pose--field system can be reduced to an equivalent pose-only system. After the optimized submap poses are obtained, the global field value at any covered location can be recovered in closed form through variance-weighted fusion of predictions from the corresponding local submaps, without training an additional global model. Experiments on simulated and real-world 2D LiDAR datasets demonstrate improved pose and occupancy-map accuracy over the evaluated baselines, reduced map storage and joining time, and improved map-construction scalability.

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