Multi-agents rely on accurate poses to share and align observations, enabling a collaborative perception of the environment. However, traditional GNSS-based localization often fails in GNSS-denied environments, making consistent feature alignment difficult in collaboration. To tackle this challenge, we propose a robust GNSS-free collaborative perception framework based on LiDAR localization. Specifically, we propose a lightweight Pose Generator with Confidence (PGC) to estimate compact pose and confidence representations. To alleviate the effects of localization errors, we further develop the Pose-Aware Spatio-Temporal Alignment Transformer (PASTAT), which performs confidence-aware spatial alignment while capturing essential temporal context. Additionally, we present a new simulation dataset, V2VLoc, which can be adapted for both LiDAR localization and collaborative detection tasks. V2VLoc comprises three subsets: Town1Loc, Town4Loc, and V2VDet. Town1Loc and Town4Loc offer multi-traversal sequences for training in localization tasks, whereas V2VDet is specifically intended for the collaborative detection task. Extensive experiments conducted on the V2VLoc dataset demonstrate that our approach achieves state-of-the-art performance under GNSS-denied conditions. We further conduct extended experiments on the real-world V2V4Real dataset to validate the effectiveness and generalizability of PASTAT.

LiDAR odometry is a critical component of SLAM in autonomous driving and robotics. Learning-based methods have shown remarkable performance by regressing relative poses in an end-to-end manner. However, when applying these trained models, originally developed on the widely used KITTI dataset, to other scenes, performance often drops significantly. In other words, existing methods struggle to generalize well to new environments. To address this challenge, we propose RCP-LO, a simple yet effective LiDAR odometry framework. We introduce a novel representation for relative poses, reformulating them as relative coordinates, which can then be solved using geometrical verification. This approach avoids overly simplified pose representations and makes better use of scene geometry, thereby improving generalization. Moreover, to capture the inherent uncertainties in relative pose estimation from occluded LiDAR point clouds from dynamic environments, we adapt our framework to learn a denoising diffusion model, allowing for sampling plausible relative coordinates while improving robustness. We also introduce a differentiable geometric weighted singular value decomposition module, enabling efficient pose estimation through a single forward pass. Extensive experiments demonstrate that RCP-LO, trained exclusively on the KITTI dataset, achieves competitive performance compared to SOTA learning-based methods and generalizes effectively to the KITTI-360, Ford, and Oxford datasets.

Language-based localization is a crucial task in robotics and computer vision, enabling robots to understand spatial positions through language. Recent methods rely on contrastive learning to establish correspondences between global features of texts and point clouds. However, the inherent ambiguity of textual descriptions makes it difficult to convey geometric information accurately, forcing alignment of them in the feature space may compromise the expressiveness of the point clouds. Unlike previous methods, this paper proposes using language as a filter to distinguish dissimilar locations. To this end, we propose a robust framework of multi-level negative contrastive learning for language-based localization, fully leveraging the descriptive power of language for spatial localization. Our method learns multiple mismatched factors by minimizing the similarity of different locations at different levels, including global-level, instance-level and relationlevel, respectively. Extensive experiments conducted on the KITTI360Pose benchmark demonstrate that our method outperforms better that the state-of-the-art methods. Specifically, we achieve a 56.3% improvement in Top-1 retrieval recall and a 45.9% improvement in 5m localization recall.

While Neural Radiance Fields (NeRFs) have advanced the frontiers of novel view synthesis (NVS) using LiDAR data, they still struggle in dynamic scenes. Due to the low frequency and sparsity characteristics of LiDAR point clouds, it is challenging to spontaneously learn a dynamic and consistent scene representation from posed scans. In this paper, we propose STGC-NeRF, a novel LiDAR NeRF method that combines spatial-temporal geometry consistency to enhance the reconstruction of dynamic scenes. First, we propose a temporal geometry consistency regularization to enhance the regression of time-varying scene geometries from low-frequency LiDAR sequences. By estimating the pointwise correspondences between synthetic (or real) and real frames at different times, we convert them into various forms of temporal supervision. This alleviates the inconsistency caused by moving objects in dynamic scenes. Second, to improve the reconstruction of sparse LiDAR data, we propose spatial geometric consistency constraints. By computing multiple neighborhood feature descriptors incorporating geometric and contextual information, we capture structural geometry information from sparse LiDAR data. This helps encourage consistent direction, smoothness, and detail of the local surface. Extensive experiments on the KITTI-360 and nuScenes datasets demonstrate that STGC-NeRF outperforms state-of-the-art methods in both geometry and intensity accuracy for dynamic LiDAR scene reconstruction.

Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long training times make these impractical for applications requiring time-sensitive system upgrades, such as autonomous driving, drones, robotics, etc. We identify large coverage areas and vast amounts of data in large-scale outdoor scenes as key challenges that limit fast training. In this paper, we propose LightLoc, the first method capable of efficiently learning localization in a new scene at light speed. Beyond freezing the scene-agnostic feature backbone and training only the scene-specific prediction heads, we introduce two novel techniques to address these challenges. First, we introduce sample classification guidance to assist regression learning, reducing ambiguity from similar samples and improving training efficiency. Second, we propose redundant sample downsampling to remove well-learned frames during training, reducing training time without compromising accuracy. In addition, the fast training and confidence estimation characteristics of sample classification enable its integration into SLAM, effectively eliminating error accumulation. Extensive experiments on large-scale outdoor datasets demonstrate that LightLoc achieves state-of-the-art performance with just 1 hour of training—50× faster than existing methods. Our Code is available at https://github.com/liw95/LightLoc.

Point cloud regression localization technology has a wide range of applications in the multimedia field. For example, in virtual reality and augmented reality, accurate point cloud localization can significantly enhance the user experience. Recently, point cloud pose regression algorithms based on APR (Absolute Pose Regression) and SCR (Scene Coordinate Regression) have achieved near sub-meter accuracy, requiring multiple repetitive trajectories for training. The key to their success lies in the diversity of viewpoints, temporal changes, and trajectories, which is resource-consuming. However, due to the errors in GPS/INS, the coupling between trajectories is not ideal, and the stability of re-localization is insufficient. Since LiDAR has covered most of the scene, single-shot localization has the potential to approach or even surpass multi-trajectory localization methods through pose enhancement. Specifically, we present Pose Enhancement Localization (PELoc), which feeds one trajectory, proposing SSDA (Single-shot Data Augmentation) and LTI (LiDAR Trajectories-coupled Interpolation) to simulate different driving poses, and we introduce KP-CL (Key Points Contrastive Learning) through feature perturbation to mitigate the differences in viewpoint/temporal phase transformations in similar scenes across different trajectories. Our algorithm has been tested on the Oxford, QE-Oxford, and NCLT datasets, where single-shot localization accuracy can approach near sub-meter level on QE-Oxford and NCLT. The code will be published in https://github.com/Eaton2022/PELoc.

Prevailing scene coordinate regression methods for LiDAR localization suffer from localization ambiguities, as distinct locations can exhibit similar geometric signatures — a challenge that current geometry-based regression approaches have yet to solve. Recent vision–language models show that textual descriptions can enrich scene understanding, supplying potential localization cues missing from point cloud geometries. In this paper, we propose GTR-Loc, a novel text-assisted LiDAR localization framework that effectively generates and integrates geospatial text regularization to enhance localization accuracy. We propose two novel designs: a Geospatial Text Generator that produces discrete pose-aware text descriptions, and a LiDAR-Anchored Text Embedding Refinement module that dynamically constructs view-specific embeddings conditioned on current LiDAR features. The geospatial text embeddings act as regularization to effectively reduce localization ambiguities. Furthermore, we introduce a Modality Reduction Distillation strategy to transfer textual knowledge. It enables high-performance LiDAR-only localization during inference, without requiring runtime text generation. Extensive experiments on challenging large-scale outdoor datasets, including QEOxford, Oxford Radar RobotCar, and NCLT, demonstrate the effectiveness of GTR-Loc. Our method significantly outperforms state-of-the-art approaches, notably achieving a 9.64%/8.04% improvement in position/orientation accuracy on QEOxford. Our code is available at https://github.com/PSYZ1234/GTR-Loc.

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 a fundamental task in robotics and computer vision, which estimates the pose of a Li- DAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR, a scene is represented as a neural network, which outputs the world coordinates for each point in the input point cloud. However, SCR treats all points equally during localization, ignoring the fact that not all objects are beneficial for localization. For exam-ple, dynamic objects and repeating structures often negatively impact SCR. To address this problem, we introduce LiSA, the first method that incorporates semantic aware-ness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parame-ters during inference, we distill the knowledge from a seg-mentation model to the original SCR network. Experi-ments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the- art methods. Applying knowledge distillation not only pre-serves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic in-formation for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.

Deep learning-based algorithms detect pavement cracks in an end-to-end manner from Mobile Laser Scanning (MLS) point clouds, achieving impressive results. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding multiscale features and the limited training data. In this paper, we propose a novel pavement crack detection framework, Crack-U2Net, which innovatively incorporates a two-level nested U-Net architecture for feature learning. This design enables the learning of intra-stage multiscale features without introducing significant memory and computation costs, resulting in substantial improvements in accuracy. Moreover, to solve the challenge of insufficient training data, we propose a Geometry-based Data Augmentation (GDA) strategy, aiming to expand the pavement dataset while preserving the pavement geometry. Extensive experiments on the Qinghai-Tibet Highway point cloud dataset demonstrate the higher accuracy and efficiency of Crack-U2Net over the state-of-the-art methods, achieving an average precision, recall, F$1\text - $score, and accuracy of 83.8%, 77.6%, 80.1%, and 95.8%, respectively.

Absolute pose regression has shown great potential in LiDAR localization, which learns to regress 6-DoF LiDAR poses through deep networks. However, recent regression methods suffer from scene ambiguities in challenging scenarios, leading to inaccurate and unstable localization. Inspired by neurobiological localization mechanisms, i.e., the firing mechanism of place cells, head-direction cells, and grid cells in mammalian brains, we propose a novel LiDAR localization framework called NIDALoc to achieve more robust and accurate results. First, we propose a Hebbian memory module, motivated by place cells, to preserve historical information, which helps refine local view features to reduce scene ambiguities. Specifically, the memory module stores scene information and then recalls it when revisiting an old place. Second, we propose a novel pose constrained framework, consisting of an orientation classification task and a grid center regression task, to regularize orientation and position estimation, respectively. The framework based on head-direction cells and grid cells constrains the absolute pose regression to reduce wrong predictions. Extensive experiments on two outdoor datasets demonstrate the effectiveness of NIDALoc, which outperforms state-of-the-art localization methods, especially in large-scale challenging scenes. The source code is available on the project website at https://github.com/PSYZ1234/NIDALoc.

LiDAR-based absolute pose regression estimates the global pose through a deep network in an end-to-end manner, achieving impressive results in learning-based localization. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding the scene geometry and the unsatisfactory quality of the data. In this work, we propose a novel LiDAR localization frame-work, SGLoc, which decouples the pose estimation to point cloud correspondence regression and pose estimation via this correspondence. This decoupling effectively encodes the scene geometry because the decoupled correspondence regression step greatly preserves the scene geometry, leading to significant performance improvement. Apart from this decoupling, we also design a tri-scale spatial feature aggregation module and inter-geometric consistency constraint loss to effectively capture scene geometry. Moreover, we empirically find that the ground truth might be noisy due to GPS/INS measuring errors, greatly reducing the pose estimation performance. Thus, we propose a pose quality evaluation and enhancement method to measure and correct the ground truth pose. Extensive experiments on the Oxford Radar RobotCar and NCLT datasets demonstrate the effectiveness of SGLoc, which outperforms state-of-the-art regression-based localization methods by 68.5% and 67.6% on position accuracy, respectively.

Deep learning for 3-D point cloud perception has been a very active research topic in recent years. A current trend is toward the combination of the semantically strong and the fine-grained information from different scales of intermediate representations to boost network generalization power and robustness against scale variation. One prominent challenge is how to effectively conduct the allocation of multiple scales of information. In this letter, we propose a module, named adaptive pyramid context fusion (APCF), to adaptively capture scales of contextual information from a multiscale feature pyramid for the point cloud. The APCF module reweights and aggregates the features from different levels in the feature pyramid via a softmax attention strategy. The allocation of information is adaptively conducted level by level from bottom to up first and then from top to bottom. To ensure both effectiveness and efficiency, we propose a multiscale context-aware network APCF-Net through applying our proposed APCF to the PointConv architecture. Experiments demonstrate that APCF-Net surpasses its vanilla counterpart by a large margin both in effectiveness and efficiency. Especially, APCF-Net outperforms state-of-the-art approaches on 3-D object classification and semantic segmentation task, with the overall accuracy of 93.3% on ModelNet40 and mIoU of 63.1% on ScanNet V2 online test.

Extracting the power lines and pylons automatically and accurately from airborne LiDAR data is a critical step in inspecting the routine power line, especially in the remote mountainous areas. However, challenges arise in using existing methods to extract the targets from large scenarios of remote mountainous areas since the terrain is undulating, and the features are difficult to distinguish. In this article, to overcome these challenges, we propose a graph convolutional network (GCN)-based method to extract power lines and pylons from Airborne LiDAR point clouds. First, data augmentation and near-ground filtering methods are developed to overcome the problems of insufficient and imbalanced samples in the LiDAR data. Then, a GCN-based framework is proposed to extract the power lines and pylons, which consist of two main modules, i.e., the neighborhood dimension information (NDI) module and the neighborhood geometry information aggregation (NGIA) module. These two modules are designed to strengthen the model’s ability to portray local geometric details. Besides, an attention fusion module is investigated to further improve the NDI and NGIA features. Finally, a line structure constraint algorithm is proposed to identify individual power lines, where the power corridor is reconstructed using a polynomial-based algorithm. Numerical experiments are conducted based on two different power line scenarios acquired in mountainous areas. The results demonstrate the superior performances of the proposed method over several existing algorithms, where the$F_{1}$score and quality of the power line are 99.3% and 98.6%, and the results of the pylon are 96% and 92.4%, respectively. The identification rate of power line identification is above 98%.

Individual tree detection is critical for forest investigation and monitoring. Several existing methods have difficulties to detect trees in complex forest environments due to insufficiently mining descriptive features. This study proposes a deep learning (DL) framework based on a designed multichannel information complementarity representation for detecting trees in complex forest using UAV laser scanning point clouds. The proposed method consists of two main stages: ground filtering and tree detection. In the first stage, a modified graph convolution network with a local topological information layer is designed to separate the ground points. Unlike most existing parametric methods, our ground filtering method avoids the optimal parameters selection to adapt to different kinds of environments. For tree detection, a top-down slice (TDS) module is first designed to mine the vertical structure information in a top-down way. Then, a special multichannel representation (MCR) is developed to preserve different distribution patterns of points from complementary perspectives. Finally, a multibranch network (MBNet) is proposed for individual tree detection by fusing multichannel features, which can provide discriminative information for MBNet to detect trees more accurately. MBNet was evaluated on seven forest areas [UAV light detection and ranging (LiDAR) data with the mean size of 14$000~\text {m}^{2}$and point density of 250 points/$\text {m}^{2}$]. Experimental results showed that the proposed framework achieves excellent performance. Our method obtains promising performance with a mean recall of 89.23% and a mean F1-score of 87.04%.

Mobile Laser Scanning (MLS) system can provide high-density and accurate 3D point clouds that enable rapid pavement crack detection for road maintenance tasks. Supervised learning-based algorithms have been proved pretty effective for handling such a large amount of inhomogeneous and unstructured point clouds. However, these algorithms often rely on a lot of annotated data, which is labor-intensive and time-consuming. This paper presents a semi-supervised point-level approach to overcome this challenge. We propose a graph-widen module to construct a reasonable graph structure for point clouds, increasing the detection performance of graph convolutional networks (GCN). The constructed graph characterizes the local features from a small amount of annotated data, avoiding information loss and dramatically reduces the dependence on annotated data. The MLS point clouds acquired by a commercial RIEGL VMX-450 system are used in this study. The experimental results demonstrate that our method outperforms the state-of-the-art point-level methods in terms of recall, F1 score, and efficiency while achieving comparable accuracy.

As a foundational preprocessing step for a lot of downstream tasks, ground filtering from airborne LiDAR data is designed to separate the ground points and preserve the off-ground points with complete shape information. However, because of the undulating terrain, it is still a challenge work to filter the ground under complex mountain regions. In this paper, we provide a deep learning based model to improve the ground filtering performance in abrupt slope using airborne LiDAR point clouds. Specifically, we first design a local topological information mining module to extract the local features. Then a modified graph convolutional networks (GCNs) is developed to fusion the local features and global features. Compared with most existing methods, our model not only enjoys the parameter-free advantage, which means it can be applied easily in various areas, but also obtains better ground filtering performance and can preserve more complete information contained in off-ground points. Experiments was implemented on seven forest areas. The proposed method obtains promising ground filtering results with mean total error of 6.46% and the mean kappa coefficient of 86.01%.

The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively.