Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a Prototype-guided Multi-task Adversarial Network (PMAN) to achieve this. Firstly, we propose an Intensity-Aware Segmentation Network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Secondly, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: a) Build an auxiliary non-parametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers; b) Introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets.

Absolute pose regression (APR) estimates global pose in an end-to-end manner, achieving impressive results in learn-based LiDAR localization. However, compared to the top-performing methods reliant on 3D-3D correspondence matching, APR's accuracy still has room for improvement. We recognize APR's lack of robust features learning and iterative denoising process leads to suboptimal results. In this paper, we propose DiffLoc, a novel framework that formulates LiDAR localization as a conditional generation of poses. First, we propose to utilize the foundation model and static-object-aware pool to learn robust features. Second, we incorporate the iterative denoising process into APR via a diffusion model conditioned on the learned geometrically robust features. In addition, due to the unique nature of diffusion models, we propose to adapt our models to two additional applications: (1) using multiple inferences to evaluate pose uncertainty, and (2) seamlessly introducing geometric constraints on denoising steps to improve prediction accuracy. Extensive experiments conducted on the Oxford Radar RobotCar and NCLT datasets demonstrate that DiffLoc outperforms better than the state-of-the-art methods. Especially on the NCLT dataset, we achieve 35% and 34.7% improvement on position and orientation accuracy, respectively. Our code is released at https://github.com/liw95/DiffLoc.

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.