MotionAwareGS-SLAM: Temporal-Consistency-Aware Observation Integration for Dynamic Gaussian SLAM

Observations useful for camera tracking and those suitable for persistent Gaussian mapping operate at fundamentally different temporal scales: a transient observation may provide a reliable pose constraint for the current frame, yet become temporally inconsistent when integrated into a persistent Gaussian map. Existing dynamic Gaussian SLAM methods mainly determine observation usability from current-frame reliability, implicitly assuming that observations beneficial for tracking are also suitable for persistent map integration. This mismatch can introduce temporally unstable information into long-term Gaussian representations even when camera tracking remains accurate. We propose MotionAwareGS-SLAM, a temporal-consistency-aware observation integration framework that incorporates temporal consistency evidence into dynamic Gaussian mapping. Specifically, we extract multi-frame temporal consistency evidence, regulate observation participation in persistent Gaussian updates, and perform asymmetric observation routing between camera tracking and persistent mapping. This design allows transient observations to contribute to robust tracking while preventing temporally unstable observations from degrading persistent Gaussian representations. Experiments on four TUM RGB-D dynamic sequences demonstrate the tracking-mapping reliability mismatch through independent future-only RGB-D and official-pose analysis, and validate the proposed observation routing through instrumented tracking and mapping pipelines. The results show that MotionAwareGS-SLAM improves persistent scene consistency while maintaining accurate camera trajectory estimation in dynamic environments.