  This paper addresses the problem of assessing trajectory quality in
conditions when no ground truth poses are available or when their accuracy is
not enough for the specific task - for example, small-scale mapping in outdoor
scenes. In our work, we propose a no-reference metric, Mutually Orthogonal
Metric (MOM), that estimates the quality of the map from registered point
clouds via the trajectory poses. MOM strongly correlates with full-reference
trajectory metric Relative Pose Error, making it a trajectory benchmarking tool
on setups where 3D sensing technologies are employed. We provide a mathematical
foundation for such correlation and confirm it statistically in synthetic
environments. Furthermore, since our metric uses a subset of points from
mutually orthogonal surfaces, we provide an algorithm for the extraction of
such subset and evaluate its performance in synthetic CARLA environment and on
KITTI dataset. The code of the proposed metric is publicly available as
pip-package.
