LAUSR

LiDAR localization is of great importance to autonomous vehicles and robotics. Absolute pose regression, directly estimating the mapping from a scene to a 6-DoF pose, has achieved impressive results in learning-based localization. Different from traditional map-based methods, it does not need a pre-built 3D map during inference. However, current regression networks typically suffer from scene ambiguities, especially in challenging traffic environments, leading to large wrong predictions (e.g., outliers) and limited applications. To address this problem, a novel LiDAR localization framework with spatio-temporal constraints is proposed, termed STCLoc, to reduce scene ambiguities and achieve more accurate localization. First, we propose to regularize regression in the spatial dimension with a novel classification task to reduce outliers. Specifically, the classification task categorizes the point cloud in terms of position and orientation and then couples it with the regression task to conduct multi-task learning. Second, to learn discriminative features to reduce scene ambiguities, we propose using attention-based feature aggregation to capture the correlation in LiDAR sequences. We conduct extensive experiments on two benchmark datasets, where the localization takes 97ms on each dataset. Results show that our model outperforms state-of-the-art methods by 43.33%/36.76% (position/orientation) on the Oxford Radar RobotCar dataset, verifying the effectiveness of our method. The source code is available on the project website at https://github.com/PSYZ1234/STCLoc.