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In environments with similar objects, autonomous robots must determine whether a detected object is a known landmark or a newly encountered one while mapping. To mitigate associated limitations, a research team developed BPDA-GMM, a Bayesian framework, that handles both data association and landmark creation simultaneously using accumulated evidence. Tests demonstrate that the approach enhances trajectory accuracy and object mapping while maintaining real-time performance on embedded hardware.