  Evaluating simultaneous localization and mapping (SLAM) algorithms
necessitates high-precision and dense ground truth (GT) trajectories. But
obtaining desirable GT trajectories is sometimes challenging without GT
tracking sensors. As an alternative, in this paper, we propose a novel
prior-assisted SLAM system to generate a full six-degree-of-freedom ($6$-DOF)
trajectory at around $10$Hz for benchmarking under the framework of the factor
graph. Our degeneracy-aware map factor utilizes a prior point cloud map and
LiDAR frame for point-to-plane optimization, simultaneously detecting
degeneration cases to reduce drift and enhancing the consistency of pose
estimation. Our system is seamlessly integrated with cutting-edge odometry via
a loosely coupled scheme to generate high-rate and precise trajectories.
Moreover, we propose a norm-constrained gravity factor for stationary cases,
optimizing pose and gravity to boost performance. Extensive evaluations
demonstrate our algorithm's superiority over existing SLAM or map-based methods
in diverse scenarios in terms of precision, smoothness, and robustness. Our
approach substantially advances reliable and accurate SLAM evaluation methods,
fostering progress in robotics research.
