  Accurate localization on autonomous driving cars is essential for autonomy
and driving safety, especially for complex urban streets and search-and-rescue
subterranean environments where high-accurate GPS is not available. However
current odometry estimation may introduce the drifting problems in long-term
navigation without robust global localization. The main challenges involve
scene divergence under the interference of dynamic environments and effective
perception of observation and object layout variance from different viewpoints.
To tackle these challenges, we present PSE-Match, a viewpoint-free place
recognition method based on parallel semantic analysis of isolated semantic
attributes from 3D point-cloud models. Compared with the original point cloud,
the observed variance of semantic attributes is smaller. PSE-Match incorporates
a divergence place learning network to capture different semantic attributes
parallelly through the spherical harmonics domain. Using both existing
benchmark datasets and two in-field collected datasets, our experiments show
that the proposed method achieves above 70% average recall with top one
retrieval and above 95% average recall with top ten retrieval cases. And
PSE-Match has also demonstrated an obvious generalization ability with a
limited training dataset.
