  Accurately generating ground truth (GT) trajectories is essential for
Simultaneous Localization and Mapping (SLAM) evaluation, particularly under
varying environmental conditions. This study introduces a systematic approach
employing a prior map-assisted framework for generating dense
six-degree-of-freedom (6-DoF) GT poses for the first time, enhancing the
fidelity of both indoor and outdoor SLAM datasets. Our method excels in
handling degenerate and stationary conditions frequently encountered in SLAM
datasets, thereby increasing robustness and precision. A significant aspect of
our approach is the detailed derivation of covariances within the factor graph,
enabling an in-depth analysis of pose uncertainty propagation. This analysis
crucially contributes to demonstrating specific pose uncertainties and
enhancing trajectory reliability from both theoretical and empirical
perspectives. Additionally, we provide an open-source toolbox
(https://github.com/JokerJohn/Cloud_Map_Evaluation) for map evaluation
criteria, facilitating the indirect assessment of overall trajectory precision.
Experimental results show at least a 30\% improvement in map accuracy and a
20\% increase in direct trajectory accuracy compared to the Iterative Closest
Point (ICP) \cite{sharp2002icp} algorithm across diverse campus environments,
with substantially enhanced robustness. Our open-source solution
(https://github.com/JokerJohn/PALoc), extensively applied in the
FusionPortable\cite{Jiao2022Mar} dataset, is geared towards SLAM benchmark
dataset augmentation and represents a significant advancement in SLAM
evaluations.
