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Accurate localization on autonomous driving cars is essential for autonomyand driving safety, especially for complex urban streets and search-and-rescuesubterranean environments where high-accurate GPS is not available. Howevercurrent odometry estimation may introduce the drifting problems in long-termnavigation without robust global localization. The main challenges involvescene divergence under the interference of dynamic environments and effectiveperception of observation and object layout variance from different viewpoints.To tackle these challenges, we present PSE-Match, a viewpoint-free placerecognition method based on parallel semantic analysis of isolated semanticattributes from 3D point-cloud models. Compared with the original point cloud,the observed variance of semantic attributes is smaller. PSE-Match incorporatesa divergence place learning network to capture different semantic attributesparallelly through the spherical harmonics domain. Using both existingbenchmark datasets and two in-field collected datasets, our experiments showthat the proposed method achieves above 70% average recall with top oneretrieval and above 95% average recall with top ten retrieval cases. AndPSE-Match has also demonstrated an obvious generalization ability with alimited training dataset.

PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding