Trajectory refinement in SLAM: the impact of Adam, AdamW, and SGD with momentum

Accurate trajectory estimation and 3D reconstruction in Simultaneous Localization and Mapping (SLAM) applications are highly dependent on the choice of optimization method, particularly with complex datasets. This study evaluates the performance of the MonoGS system using three different optimizers: Adaptive Moment Estimation (Adam), AdamW, and SGD with Momentum. Originally utilizing Adam for its rapid convergence and reliability, we aimed to improve the system's accuracy and reconstruction quality by replacing Adam with AdamW and SGD with Momentum. Experiments conducted using the TUM-Mono dataset revealed that AdamW, which incorporates a weight decay parameter, significantly enhances generalization and reduces overfitting. Our results indicate that AdamW achieved a lower Absolute Trajectory Error (ATE) Root Mean Squared Error (RMSE) of 0.02588, compared to Adam's 0.03594, leading to more accurate and structured 3D reconstructions. In contrast, SGD with Momentum yielded the highest error of 0.05911. These findings highlight AdamW’s superior efficacy in improving SLAM robustness and accuracy, demonstrating its potential as the optimal choice among the tested optimizers.