Autonomous Underwater Vehicles (AUVs) encounter significant challenges in confined spaces like ports and testing tanks, where vehicle-environment interactions, such as wave reflections and unsteady flows, introduce complex, time-varying disturbances. Model-based state estimation methods can struggle to handle these dynamics, leading to localization errors. To address this, we propose a data-driven velocity estimation approach using Inertial Measurement Units (IMUs) and a Gated Recurrent Unit (GRU) neural network, capturing temporal dependencies and rejecting external disturbances. This velocity estimator is then integrated into a sensor fusion framework using an asynchronous Kalman filter to improve localization by fusing on-board and off-board sensor information. Experimental validation on miniature AUVs demonstrates the effectiveness of the proposed method in enhancing accuracy for velocity and position estimation in environments with significant disturbances due to interactions between the vehicle and the environment.

Existing indoor SLAM datasets primarily focus on robot sensing, often lacking building architectures. To address this gap, we design and construct the first dataset to couple the SLAM and BIM, named SLABIM. This dataset provides BIM and SLAM -oriented sensor data, both modeling a university building at HKUST. The as-designed BIM is decomposed and converted for ease of use. We employ a multi-sensor suite for multi-session data collection and mapping to obtain the as-built model. All the related data are timestamped and organized, enabling users to deploy and test effectively. Furthermore, we deploy advanced methods and report the experimental results on three tasks: registration, localization and semantic mapping, demonstrating the effectiveness and practicality of SLAB 1M. We make our dataset open-source at https://github.com/HKUST-Aerial-Robotics/SLABIM.

Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient exploration strategies. To address these limitations, we propose Bayesian Embodied Image-goal Navigation Using Gaussian Splatting, a novel method that formulates ImageNav as an optimal control problem within a model predictive control framework. BEINGS leverages 3D Gaussian Splatting as a scene prior to predict future observations, enabling efficient, real-time navigation decisions grounded in the robot's sensory experiences. By integrating Bayesian updates, our method dynamically refines the robot's strategy without requiring extensive prior experience or data. Our algorithm is validated through extensive simulations and physical experiments, showcasing its potential for embodied robot systems in visually complex scenarios. Project Page: www.mwg.ink/BEINGS-web.

In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation and loop closing. To address these issues, we first propose leveraging a low-cost single-beam sonar to improve scale estimation. Then, VIMS integrates a high-sampling-rate magnetometer for place recognition by utilizing magnetic signatures generated by an economical magnetic field coil. Building on this, a hierarchical scheme is developed for visual-magnetic place recognition, enabling robust loop closure. Furthermore, VIMS achieves a balance between local feature tracking and descriptor-based loop closing, avoiding additional computational burden on the front end. Experimental results highlight the efficacy of the proposed VIMS, demonstrating significant improvements in both the robustness and accuracy of state estimation within underwater environments.

Light detection and ranging (LiDAR) point clouds and building information modeling (BIM) represent two distinct data modalities in the fields of robot perception and construction. These modalities originate from different sources and are associated with unique reference frames. The primary goal of this study is to align these modalities within a shared reference frame using a global registration approach, effectively enabling them to “speak the same language”. To achieve this, we propose a cross-modality registration method, spanning from the front end to the back end. At the front end, we extract triangle descriptors by identifying walls and intersected corners, enabling the matching of corner triplets with a complexity independent of the BIM’s size. For the back-end transformation estimation, we utilize the Hough transform to map the matched triplets to the transformation space and introduce a hierarchical voting mechanism to hypothesize multiple pose candidates. The final transformation is then verified using our designed occupancy-aware scoring method. To assess the effectiveness of our approach, we conducted real-world multi-session experiments in a large-scale university building, employing two different types of LiDAR sensors. We make the collected datasets and codes publicly available to benefit the community. Note to Practitioners—Our proposed registration method leverages walls and corners as shared features between LiDAR and BIM data, making it particularly well-suited for scenarios with well-defined structural layouts. Accumulating a larger LiDAR submap provides richer structural information, which further aids in achieving accurate alignment. To optimize computational efficiency, we recommend constructing the descriptor database offline and loading it during runtime, enabling a theoretical retrieval complexity of$O(1)$. Despite its advantages, our approach has certain limitations. First, it primarily focuses on planar structures, which limits its effectiveness in utilizing non-planar features. Second, the method may underperform in cases where significant deviations exist between the as-designed BIM and as-is LiDAR data. Lastly, in ambiguous scenarios, such as long corridors or similar layouts within the same or across different floors, our method may struggle to verify the correct transformation among candidates. To address these challenges, incorporating additional information, particularly semantic cues such as floor numbers, room numbers, and room types, could enhance its robustness and reliability.

This study introduces a novel framework, G3Reg, for fast and robust global registration of LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we extract fundamental geometric primitives, including planes, clusters, and lines (PCL) from the raw point cloud to obtain low-level semantic segments. Each segment is represented as a unified Gaussian Ellipsoid Model (GEM), using a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR). Specifically, we establish an upper bound, which can be traversed based on the confidence level for compatibility testing to construct the pyramid graph. Then, we solve multiple maximum cliques (MAC) for each level of the pyramid graph, thus generating the corresponding transformation candidates. In the verification phase, we adopt a precise and efficient metric for point cloud alignment quality, founded on geometric primitives, to identify the optimal candidate. The algorithm’s performance is validated on three publicly available datasets and a self-collected multi-session dataset. Parameter settings remained unchanged during the experiment evaluations. The results exhibit superior robustness and real-time performance of the G3Reg framework compared to state-of-the-art methods. Furthermore, we demonstrate the potential for integrating individual GEM and PAGOR components into other registration frameworks to enhance their efficacy.Note to Practitioners—Our proposed method aims to perform global registration for outdoor LiDAR point clouds. Our methodology, which extracts point cloud segments and utilizes their centers for registration, differs from conventional approaches that rely on keypoints and descriptors. We further propose GEM to model the uncertainty of the centers and embed it into our distrust-and-verify framework. In theory, our method can be applied to any registration task that involves primitives representable as sets of Gaussians or points. Additionally, practitioners should consider the following to enhance applicability. First, practitioners can fine-tune the parameters of the segmentation algorithm to generate more repeatable segmentation results. Second, although our default setting uses four compatibility test thresholds, fewer may suffice, especially when translations between point clouds are minor. Finally, for geometrically uninformative segments such as vegetation, consider extracting descriptors within these segments to increase correspondences.

Light detection and ranging (LiDAR) point cloud maps are extensively utilized on roads for robot navigation due to their high consistency. However, dense point clouds face challenges of high memory consumption and reduced maintainability for long-term operations. In this study, we introduce scalable and lightweight LiDAR mapping (SLIM), a scalable and lightweight mapping system for long-term LiDAR mapping in urban environments. The system begins by parameterizing structural point clouds into lines and planes. These lightweight and structural representations meet the requirements of map merging, pose graph optimization, and bundle adjustment, ensuring incremental management and local consistency. For long-term operations, a map-centric nonlinear factor recovery method is designed to sparsify poses while preserving mapping accuracy. We validate the SLIM system with multisession real-world LiDAR data from classical LiDAR mapping datasets, including KITTI, NCLT, HeLiPR, and M2DGR. The experiments demonstrate its capabilities in mapping accuracy, lightweightness, and scalability. Map reuse is also verified through map-based robot localization. Finally, with multisession LiDAR data, the SLIM system provides a globally consistent map with low memory consumption ($\sim$130 KB/km on KITTI).

Radar offers the advantage of providing additional physical properties related to observed objects. In this study, we design a physical-enhanced radar-inertial odometry system that capitalizes on the Doppler velocities and radar cross-section information. The filter for static radar points, correspondence estimation, and residual functions are all strengthened by integrating the physical properties. We conduct experiments on both public datasets and our self-collected data, with different mobile platforms and sensor types. Our quantitative results demonstrate that the proposed radar-inertial odometry system outperforms alternative methods using the physical-enhanced components. Our findings also reveal that using the physical properties results in fewer radar points for odometry estimation, but the performance is still guaranteed and even improved, thus aligning with the "less is more" principle.

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users‘ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication archi-tecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%.

In this study, we propose a novel system for real-time coloring LiDAR point clouds with a low-cost RS camera. The main challenges are dealing with the motion distortion of the RS camera and the multi-sensor time synchronization. To tackle these challenges, we carefully design a hardware synchronizer to ensure the strict alignment of the LiDAR, inertial measurement unit, and RS camera. With accurate timestamps, we first use LiDAR-inertial odometry (LIO) for pose estimation, and the poses of image line exposure are calculated by forward propagation based on a constant velocity motion model. Then, we propose our method based on the RS constraint for colorizing the LiDAR point cloud. For comparison, we colorize the LiDAR point cloud with conventional rolling shutter image undistortion. In the real-world tests, The results show that our proposed method produces more accurate and efficient colorization of point clouds. Besides, considering the situation of readout time not being provided, we propose a method to calibrate the readout time by minimizing the reprojection error of LIO's inter-frame pose and image optical flows. We release our code and self-collected datasets on Github33https://github.com/sheng00125/Rollvox to benefit the community.

In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges by incorporating Chamfer distance, pose uncertainty, and lateral sequence consistency. Furthermore, we meticulously design control point initialization, spline parameterization, and optimization to progressively create, expand, and refine splines. In contrast to prior research that assessed performance using self-constructed datasets, our experiments are conducted on the openly accessible OpenLane dataset. The experimental outcomes reveal that our suggested approach enhances lane association and odometry precision, as well as overall lane map quality. We have open-sourced out code11https://github.com/HKUST-Aerial-Robotics/MonoLaneMapping for this project.

Global point cloud registration is essential in many robotics tasks like loop closing and relocalization. Unfortunately, the registration often suffers from the low overlap between point clouds, a frequent occurrence in practical applications due to occlusion and viewpoint change. In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap. To this end, we construct a consistency graph to facilitate robust data association and employ graduated non-convexity (GNC) for reliable pose estimation, following the state-of-the-art (SoTA) methods. Unlike previous approaches, we use semantic cues to scale down the dense point clouds, thus reducing the problem size. Moreover, we address the ambiguity arising from the consistency threshold by constructing a pyramid graph with multi-level consistency thresholds. Then we propose a cascaded gradient ascend method to solve the resulting densest clique problem and obtain multiple pose candidates for every consistency threshold. Finally, fast geometric verification is employed to select the optimal estimation from multiple pose candidates. Our experiments, conducted on a self-collected indoor dataset and the public KITTI dataset, demonstrate that our method achieves the highest success rate despite the low overlap of point clouds and low semantic quality. We have open-sourced our code1for this project.

In this paper, we present a centralized framework for multi-session LiDAR mapping in urban environments, by utilizing lightweight line and plane map representations instead of widely used point clouds. The proposed framework achieves consistent mapping in a coarse-to-fine manner. Global place recognition is achieved by associating lines and planes on the Grassmannian manifold, followed by an outlier rejection-aided pose graph optimization for map merging. Then a novel bundle adjustment is also designed to improve the local consistency of lines and planes. In the experimental section, both public and self-collected datasets are used to demonstrate efficiency and effectiveness. Extensive results validate that our LiDAR mapping framework could merge multi-session maps globally, optimize maps incrementally, and is applicable for lightweight robot localization.

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density.

Compared to the onboard camera and laser scanner, radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization under adverse conditions. However, radar data is sparse and noisy, resulting in challenges for radar mapping. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for exhaustive similarity evaluation against the current radar modal, yielding the regression of the current pose. Finally, we apply this differentiable measurement model to a Kalman Filter (KF) to learn the whole sequential localization process in an end-to-end manner.The whole learning system is differentiable with the network based measurement model at the front-endand KF at the back-end. To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over$90km$driving, even in generalization scenarios where the model training is in UK, while testing in South Korea. We also release the source code publicly.

In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning within tens of milliseconds. As in-flight uneven objects are affected by various kinds of forces, which leads to the time-varying acceleration, motion prediction for them is difficult. In order to compensate the system’s non-linearity, we propose using a recurrent neural network model, which we call the Neural Acceleration Estimator (NAE), to estimate the varying acceleration by observing a small fragment of previous deflected trajectory without any prior information. Moreover, end-to-end training with Differantiable Filter (NAE-DF) gives a supervision for measurement uncertainty and further improves the prediction accuracy. Experimental results show that motion prediction with NAE and NAE-DF is superior to other methods and has a good generalization performance on unseen objects. We test our methods on a robot, performing velocity control in real world and respectively achieve 83.3% and 86.7% success rate on a ploy urethane banana and a gourd. We also release an object in-flight dataset containing 1,500 trajectorys for uneven objects, which can be found on the project website:https://sites.google.com/view/neural-motion-prediction.

Large scale 3D maps constructed via LiDAR sensor are widely used on intelligent vehicles for localization in outdoor scenes. However, loading, communication and processing of the original dense maps are time consuming for onboard computing platform, which calls for a more concise representation of maps to reduce the complexity but keep the performance of localization. In this paper, we propose a teacher-student learning paradigm to compress the 3D point cloud map. Specifically, we first find a subset of LiDAR points with high number of observations to preserve the localization performance, which is regarded as the teacher of map compression. An efficient optimization strategy is proposed to deal with the massive data in original map. With the supervision of compressed map, a student model is built by training a random forest model fed with geometric feature descriptors of each point. As a result, the student model is able to compress the map without referring to the expensive numerical optimization. Additionally, by incorporating the features, the innovative student model can be generalized to other new maps while no re-training is required. We conduct thorough experiments on multi-session dataset and KITTI dataset to demonstrate the effectiveness and efficiency of the proposed learning paradigm, and the comparison with other map compression methods. The final results show that the learned student model can achieve efficient map compression with comparable LiDAR based localization performance to the original map at the same time.

Autonomous mobile vehicles are expected to perform persistent and accurate localization with low-cost equipment. To achieve this goal, we propose a stereo camera based visual localization method using a modified laser map, which takes the advantage of both the low cost of camera, and high geometric precision of laser data to achieve long-term performance. Considering that LiDAR and camera give measurements of the same environment in different modalities, the cross-modal invariance is investigated to modify the laser map for visual localization. Specifically, a map learning algorithm is introduced to sample the robust subsets in laser maps that are useful for visual localization using multi-session visual and laser data. Further, a generative map model is derived to describe this cross-modal invariance, based on which two types of measurements are defined to model the laser map points as appropriate visual observations. Tightly coupling these measurements within the local bundle adjustment during online sliding-window based visual odometry, the vehicle can achieve robust localization even one year after the map was built. The effectiveness of the proposed method is evaluated on both the public KITTI datasets and self-collected datasets in our campus, which include seasonal, illumination and object variations. On all experimental localization sessions, our method provides satisfactory results, even when the direction is opposite to that in the mapping session, verifying the superior performance of the laser map based visual localization method.

Global localization in 3D point clouds is a challenging task for mobile vehicles in outdoor scenarios, which requires the vehicle to localize itself correctly in a given map without prior knowledge of its pose. This is a critical component of autonomous vehicles or robots on the road for handling localization failures. In this paper, based on reduced dimension scan representations learned from neural networks, a solution to global localization is proposed by achieving place recognition first and then metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted feature learning method for 3D Light detection and ranging (LiDAR) point clouds using artificial statistics and siamese network, which transforms the place recognition problem into a similarity modeling problem. Additionally, the sensor data using dimension reduced representations require less storage space and make the searching easier. With the learned representations by networks and the global poses, a prior map is built and used in the localization framework. In the localization step, position only observations obtained by place recognition are used in a particle filter algorithm to achieve precise pose estimation. To demonstrate the effectiveness of our place recognition and localization approach, KITTI benchmark and our multi-session datasets are employed for comparison with other geometric-based algorithms. The results show that our system can achieve both high accuracy and efficiency for long-term autonomy.

The precisely and timely extraction of burned areas after forest fire plays an important role in reducing disaster losses, maintaining ecological balance and protecting forest resources. In this article, we propose a method of automatically extracting forest fire burned areas by using Gaofen-1 remote sensing images. Firstly, the geometric correction and atmospheric correction are carried out in Gaofen-1 WFV (Wide Field Viewer) images, and then the normalized vegetation index is calculated using the nearinfrared band and red band. In the vegetation index results, the Otsu's method is used to set the threshold value to automatically obtain the burned areas. Taking the forest fire that occurred in the Daxing'anling Khanma Nature Reserve on June 2, 2018 as an example, the method can extract the burned areas fully automatically and the precision is better than 94%.

With the rapid improvement of optical payload radiometric calibration accuracy, it is necessary to carry out in-orbit radiometric calibration to improve the calibration accuracy and image quality. The design and analysis of in-orbit radiometric calibration with moon, sun and side-slither is presented. The side-slither radiometric calibration mission based on along track scanning is proposed to obtain 60 angles polarization Stokes parameters with the calibration target of deep convective cloud. The satellite attitude stability for in-orbit radiometric calibration is analyzed and meets the requirements of optical image quality.

Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targeting at long-term localization in real time. On the robot, we employ two estimators to achieve accurate and real-time performance. One is a sliding-window based visual inertial odometry, which integrates constraints from consecutive observations and self-motion measurements, as well as the constraints induced by localization results from the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the high-frequency inertial measurements. On the cloud, we propose a longer sliding-window based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve drift-free and real-time localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in real-world environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions.

Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geometry information of the laser map, a hybrid bundle adjustment framework is proposed, which estimates the poses of the cameras with respect to the prior laser map as well as optimizes the state variables of the online visual inertial odometry system simultaneously. For more accurate crossmodal data association, the laser map is optimized using multisession laser and visual data to extract the salient and stable subset for visual localization. To validate the efficiency of the proposed method, we collect data in south part of our campus in different seasons, along the same and opposite-direction route. In all sessions of localization data, our proposed method gives satisfactory results, and shows the superiority of the hybrid bundle adjustment and map optimization1.

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.

The parcel-based changed detection by adopting the holistic feature can extract the changed parcels in land-use maps[1]. This method of parcel-based change detection uses the holistic feature, which represents each land use parcels clipped by polygons in the land use map with the energy spectrum of WFT and extracts the changed parcels according to the distance threshold between feature vectors associated with pairs of corresponding parcels. In this procedure, a key point is deriving the “Spatial Envelope” feature of each land-use parcel segmented by the land use map, so the three parameters used to calculate this feature will surely influence the descriptor and the change detection results. In this article, we conduct experimental analysis to analyze the influence of different parameters on the results in order to find out the optimal parameters for parcel-based change detection. After comparisons and analysis, we have concluded that the optimal scale number parameter is 4, the optimal angle number parameter is 8 and the optimal block number parameter is 16 blocks(4*4) for parcel-based change detection.