Point cloud place recognition (PCPR) determines the geo-location within a prebuilt map and plays a crucial role in photogrammetry and robotics applications such as autonomous driving, intelligent transportation, and augmented reality. In real-world large-scale deployments of a geographic positioning system, PCPR models must continuously acquire, update, and accumulate knowledge to adapt to diverse and dynamic environments, i.e., the ability known as continual learning (CL). However, existing PCPR models often suffer from catastrophic forgetting, leading to significant performance degradation in previously learned scenes when adapting to new environments or sensor types. This results in poor model scalability, increased maintenance costs, and system deployment difficulties, undermining the practicality of PCPR. To address these issues, we propose LifelongPR, a novel continual learning framework for PCPR, which effectively extracts and fuses knowledge from sequential point cloud data. First, to alleviate the knowledge loss, we propose a replay sample selection method that dynamically allocates sample sizes according to each dataset’s information quantity and selects spatially diverse samples for maximal representativeness. Second, to handle domain shifts, we design a prompt learning-based CL framework with a lightweight continuous prompt module and a two-stage training strategy, enabling domain-specific feature adaptation while minimizing forgetting. Comprehensive experiments on large-scale public and self-collected datasets are conducted to validate the effectiveness of the proposed method. Compared with the state-of-the-art (SOTA) method, our method achieves 6.50% improvement in$mIR\text{@}1$, 7.96% improvement in$mR\text{@}1$, and an 8.95% reduction in$F$. The code and pre-trained models are publicly available athttps://zouxianghong.github.io/LifelongPR

Robot navigation in dense human crowds poses a significant challenge due to the complexity of human behavior in dynamic and obstacle-rich environments. In this work, we propose a dynamic weight adjustment scheme using a neural network to predict the optimal weights of objectives in an optimization-based motion planner. We adopt a spatial-temporal trajectory planner and incorporate diverse objectives to achieve a balance among safety, efficiency, and goal achievement in complex and dynamic environments. We design the network structure, observation encoding, and reward function to effectively train the policy network using reinforcement learning, allowing the robot to adapt its behavior in real time based on environmental and pedestrian information. Simulation results show improved safety compared to the fixed-weight planner and the state-of-the-art learning-based methods, and verify the ability of the learned policy to adaptively adjust the weights based on the observed situations. The feasibility of the approach is demonstrated in a navigation task using an autonomous delivery robot across a crowded corridor over a 300 m distance. Video: https://youtu.be/nSCbNaaF_VM

Helmet-mounted wearable positioning systems are crucial for enhancing safety and facilitating coordination in industrial, construction, and emergency rescue environments. These systems, including LiDAR-Inertial Odometry (LIO) and Visual-Inertial Odometry (VIO), often face challenges in localization due to adverse environmental conditions such as dust, smoke, and limited visual features. To address these limitations, we propose a novel head-mounted Inertial Measurement Unit (IMU) dataset with ground truth, aimed at advancing data-driven IMU pose estimation. Our dataset captures human head motion patterns using a helmet-mounted system, with data from ten participants performing various activities. We explore the application of neural networks, specifically Long Short-Term Memory (LSTM) and Transformer networks, to correct IMU biases and improve localization accuracy. Additionally, we evaluate the performance of these methods across different IMU data window dimensions, motion patterns, and sensor types. We release a publicly available dataset, demonstrate the feasibility of advanced neural network approaches for helmet-based localization, and provide evaluation metrics to establish a baseline for future studies in this field. Data and code can be found at https://lqiutong.github.io/HelmetPoser.github.io/.

Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seaports. The main challenges are calibration and localization under obstructed conditions, which are common in logistics environments. Traditional calibration methods, dependent on line-of-sight (LoS), are slow, costly, and unreliable in seaports and warehouses, making large-scale localization a significant pain point in the industry. To overcome these challenges, we propose a one-shot calibration and localization framework based on UWB-LiDAR fusion. Our method uses Gaussian processes to estimate the anchor position from continuous-time LiDAR Inertial Odometry with sampled UWB ranges. This approach ensures accurate and reliable calibration with only one round of sampling in large-scale areas, i.e.,$600 \times 450 ~\mathrm{m}^{2}$. With LoS issues, UWB-only localization can be problematic, even when anchor positions are known. We demonstrate that by applying a UWB-range filter, the search range for LiDAR loop closure descriptors is significantly reduced, improving both accuracy and speed. This concept can be applied to other loop closure detection methods, enabling cost-effective localization in large-scale warehouses and seaports. It significantly improves precision in challenging environments where the UWB-only and LiDAR-Inertial methods fail, as shown in the video https://https://youtu.be/oY8jQKdM7lU. We will open-source our datasets and calibration codes for community use.

Conventional single LiDAR systems are inherently constrained by their limited field of view (FoV), leading to blind spots and incomplete environmental awareness, particularly on robotic platforms with strict payload limitations. Integrating a motorized LiDAR offers a practical solution by significantly expanding the sensor’s FoV and enabling adaptive panoramic 3D sensing. However, the high-frequency vibrations of the quadruped robot introduce calibration challenges: these oscillations continually disturb the LiDAR–motor extrinsics, so parameters calibrated once may drift during operation and degrade sensing accuracy.Existing calibration methods that use artificial targets or dense feature extraction lack feasibility for on-site applications and real-time implementation. To overcome these limitations, we propose LiMo-Calib, an efficient on-site calibration method that eliminates the need for external targets by leveraging geometric features directly from raw LiDAR scans. LiMo-Calib optimizes feature selection based on normal distribution to accelerate convergence while maintaining accuracy and incorporates a reweighting mechanism that evaluates local plane fitting quality to enhance robustness. We integrate and validate the proposed method on a motorized LiDAR system mounted on a quadruped robot, demonstrating significant improvements in calibration efficiency and 3D sensing accuracy, making LiMo-Calib well-suited for real-world robotic applications. We further demonstrate the accuracy improvements of the Lidar Inertial Odometry (LIO) on the panoramic 3D sensing system using the calibrated parameters. The code will be available at: https://github.com/kafeiyin00/LiMo-Calib.

Large-scale LiDAR Bundle Adjustment (LBA) to refine sensor orientation and point cloud accuracy simultaneously for building navigation maps is a fundamental task in logistics, intelligent transportation, and robotics. In the context of autonomous delivery and smart mobility, the 3D map obtained by accurate and robust LBA plays a pivotal role in enabling reliable localization and navigation across complex, large-scale urban environments. Unlike pose-graph-based methods that rely solely on pairwise relationships between LiDAR frames, LBA leverages raw LiDAR correspondences to achieve more precise results, especially when initial pose estimates are unreliable for low-cost sensors. However, existing LBA methods face challenges such as simplistic planar correspondences, extensive observations, and dense normal matrices in the least-squares problem, which limit robustness, efficiency, and scalability. To address these issues, we propose a Graph Optimality-aware Stochastic Optimization scheme with Progressive Spatial Smoothing, namely PSS-GOSO, to achieverobust,efficient, andscalableLBA. The Progressive Spatial Smoothing (PSS) module extractsrobustLiDAR feature association exploiting the prior structure information obtained by the polynomial smooth kernel. The Graph Optimality-aware Stochastic Optimization (GOSO) module first sparsifies the graph according to optimality for anefficientoptimization. GOSO then utilizes stochastic clustering and graph marginalization to solve the large-scale state estimation problem for ascalableLBA. We validate PSS-GOSO across diverse scenes captured by various platforms, demonstrating its superior performance compared to existing methods. Moreover, the resulting point cloud maps are used for automatic last-mile delivery in large-scale complex scenes, showcasing the practical benefits of our method in modern intelligent transportation systems. The project page can be found at:https://kafeiyin00.github.io/PSS-GOSO/

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. MMAUD stands out by combining diverse sensory inputs, including stereo vision, various Lidars, Radars, and audio arrays. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Additionally, MMAUD provides accurate Leica-generated ground truth data, enhancing credibility and enabling confident refinement of algorithms and models, which has never been seen in other datasets. Most existing works do not disclose their datasets, making MMAUD an invaluable resource for developing accurate and efficient solutions. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools. Our dataset closely simulates real-world scenarios by incorporating ambient heavy machinery sounds. This approach enhances the dataset’s applicability, capturing the exact challenges faced during proximate vehicular operations. It is expected that MMAUD can play a pivotal role in advancing UAV threat detection, classification, trajectory estimation capabilities, and beyond. Our dataset, codes, and designs will be available in https://ntu-aris.github.io/MMAUD.

Accurate and consistent construction of point clouds from LiDAR scanning data is fundamental for 3D modeling applications. Current solutions, such as multiview point cloud registration and LiDAR bundle adjustment, predominantly depend on the local plane assumption, which may be inadequate in complex environments lacking of planar geometries or substantial initial pose errors. To mitigate this problem, this paper presents a LiDAR bundle adjustment with progressive spatial smoothing, which is suitable for complex environments and exhibits improved convergence capabilities. The proposed method consists of a spatial smoothing module and a pose adjustment module, which combines the benefits of local consistency and global accuracy. With the spatial smoothing module, we can obtain robust and rich surface constraints employing smoothing kernels across various scales. Then the pose adjustment module corrects all poses utilizing the novel surface constraints. Ultimately, the proposed method simultaneously achieves fine poses and parametric surfaces that can be directly employed for high-quality point cloud reconstruction. The effectiveness and robustness of our proposed approach have been validated on both simulation and real-world datasets. The experimental results demonstrate that the proposed method outperforms the existing methods and achieves better accuracy in complex environments with low planar structures.

Point cloud semantic segmentation (PCSS) shows great potential in generating accurate 3D semantic maps for digital twin railways. Deep learning-based methods have seen substantial advancements, driven by numerous PCSS datasets. Nevertheless, existing datasets tend to neglect railway scenes, with limitations in scale, categories, and scene diversity. This motivated us to establish WHU-Railway3D, a diverse PCSS dataset specifically designed for railway scenes. WHU-Railway3D is categorized into urban, rural, and plateau railways based on scene complexity and semantic class distribution. The dataset spans approximately 30 km with 4.6 billion points labeled into 11 classes, such as rails, masts, overhead lines, and fences. In addition to 3D coordinates, WHU-Railway3D provides rich attribute information such as reflected intensity, scanning angle, and number of returns. Cutting-edge methods are extensively evaluated on the dataset, followed by in-depth analysis. Lastly, key challenges and potential future work are identified to stimulate further innovative research. The dataset is accessible athttps://github.com/WHU-USI3DV/WHU-Railway3D.

Real-time 3D mapping of large-scale Global Navigation Satellite System (GNSS)-denied environments plays an important role in forest inventory management, disaster emergency response, and underground facility maintenance. Compact helmet laser scanning (HLS) systems keep the same direction as the user’s line of sight and have the advantage of “what you see is what you get”, providing a promising and efficient solution for 3D geospatial information acquisition. However, the violent motion of the helmet, the limited field of view of the laser scanner, and the repeated symmetrical geometric structures in GNSS-denied environments pose enormous challenges for the existing simultaneous localization and mapping (SLAM) algorithms. To promote the development of HLS and explore its application in large-scale GNSS-denied environments, the first large-scale HLS dataset covering multiple difficult GNSS-denied areas (e.g., forests, mountains, underground spaces) was built in this study. Besides using an additional very high accuracy fiber-optic inertial measurement unit (IMU), a novel post-processing multi-source fusion method—progressive trajectory correction (PTC)—is proposed to generate a reliable ground-truth trajectory for the benchmark, which overcomes the problems of scan matching degradation and non-rigid distortion. The accuracies of the ground truth are controlled and checked by manually surveyed feature points along the trajectory. Finally, the existing state-of-the-art SLAM methods were evaluated on the WHU-Helmet dataset, summarizing the future HLS SLAM research trends. The full dataset is available for download at: https: //github.com/kafeiyin00/WHU-HelmetDataset.

Rigorous boresight calibration between light detection and ranging (LiDAR) and the camera is crucial for geometry and optical information fusion in earth observation and robotic applications. Although boresight parameters can be obtained through pre-calibration with artificial targets, unforeseen movement of sensors during data collection can lead to significant errors in the boresight parameters. To address this issue, we propose SE-Calib, an automatic and target-free online boresight calibration method for LiDAR-Camera systems. SE-Calib firstly extracts semantic edge features from both point clouds and images simultaneously using the 3D semantic segmentation (3D-SS) and 2D semantic edge detection (2D-SED) methods. The boresight parameters are then optimized with an adaptive solver and maximizing the Soft Semantic Response Consistency Metric (SSRCM) scores iteratively. The SSRCM is designed to evaluate the coherence of cross-modular semantic edge features, and a confidence function is proposed to filter out unreliable optimization results. Experiments conducted on challenging urban datasets show an average boresight error of 0.206 degrees (2.47 pixels in reprojection error), demonstrating the effectiveness and robustness of the proposed method.