In Indoor Mobile Mapping Systems (iMMS) the trajectory estimation is implemented by the SLAM (Simultaneous Localization and Mapping) algorithm. By assuming a fixed environment surrounding the instrument, the algorithm relies on stable geometries to establish the trajectory. Drift effects represent the main source for errors and affect the trajectory estimation. These effects can be magnified in feature-deficient or degenerate environments, where the variation of geometrical elements can be minimal, as in the case of tunnels. In this context, difficult environments such as tunnels are suitable for the implementation of alternative algorithms for the trajectory estimation. Considering this kind of scenario, the contribution has the twofold objective of evaluating the results of two trajectory estimation methods, in terms of trajectory drift, with reference to an indoor SLAM-based MMS, and to establish a repeatable methodology to do so. A novel algorithm for the trajectory estimation, not just relying on geometrical SLAM algorithm, but also taking advantage of reflectance images coming from LiDAR sensors mounted on the system, is considered. The case study is a 200 m long branch of a motor-way tunnel, with a diameter of 15 m. The test is further subdivided by computing all trajectories with different constraining strategies, first without any constraints, then considering global optimisation, loop closure and static control scans, to replicate typical realistic scenarios in tunnel mapping. The results of this work highlight how the novel reflectance-aided SLAM algorithm is beneficial in terms of drift reduction in the estimated trajectories.