Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the retrieval of contextually flawed or incomplete information. To address this, knowledge graph-based RAG methods have evolved towards hierarchical structures, organizing knowledge into multi-level summaries. However, these approaches still suffer from two critical, unaddressed challenges: high-level conceptual summaries exist as disconnected ``semantic islands'', lacking the explicit relations needed for cross-community reasoning; and the retrieval process itself remains structurally unaware, often degenerating into an inefficient flat search that fails to exploit the graph's rich topology. To overcome these limitations, we introduce LeanRAG, a framework that features a deeply collaborative design combining knowledge aggregation and retrieval strategies. LeanRAG first employs a novel semantic aggregation algorithm that forms entity clusters and constructs new explicit relations among aggregation-level summaries, creating a fully navigable semantic network. Then, a bottom-up, structure-guided retrieval strategy anchors queries to the most relevant fine-grained entities and then systematically traverses the graph's semantic pathways to gather concise yet contextually comprehensive evidence sets. The LeanRAG can mitigate the substantial overhead associated with path retrieval on graphs and minimize redundant information retrieval. Extensive experiments on four challenging QA benchmarks with different domains demonstrate that LeanRAG significantly outperforms existing methods in response quality while reducing 46% retrieval redundancy.

Multimodal reasoning in Large Language Models (LLMs) struggles with incomplete knowledge and hallucination artifacts, challenges that textual Knowledge Graphs (KGs) only partially mitigate due to their modality isolation. While Multimodal Knowledge Graphs (MMKGs) promise enhanced cross-modal understanding, their practical construction is impeded by semantic narrowness of manual text annotations and inherent noise in visual-semantic entity linkages. In this paper, we propose Vision-align-to-Language integrated Knowledge Graph (VaLiK), a novel approach for constructing MMKGs that enhances LLMs reasoning through cross-modal information supplementation. Specifically, we cascade pre-trained Vision-Language Models (VLMs) to align image features with text, transforming them into descriptions that encapsulate image-specific information. Furthermore, we developed a cross-modal similarity verification mechanism to quantify semantic consistency, effectively filtering out noise introduced during feature alignment. Even without manually annotated image captions, the refined descriptions alone suffice to construct the MMKG. Compared to conventional MMKGs construction paradigms, our approach achieves substantial storage efficiency gains while maintaining direct entity-to-image linkage capability. Experimental results on multimodal reasoning tasks demonstrate that LLMs augmented with VaLiK outperform previous state-of-the-art models. Our code is published at https://github.com/Wings-Of-Disaster/VaLiK.

Extrinsic calibration for LiDAR and camera is an essential prerequisite for sensor fusion. Recently, automatic and target-less extrinsic calibration has become the mainstream of academic research. However, geometric feature-based methods still have requirements on the scene. Deep learning methods, while achieving high accuracy and good adaptability, rely on large annotated dataset and need additional training. We propose a novel LiDAR-camera calibration method by using the Segment Anything Model(SAM) without additional training. With the automatically generated masks, we optimize the extrinsic parameters by maximizing the consistency score of the point attributes that fall on each mask. The point cloud attributes include intensity, normal vector and segmentation class. Experiments on different real-world dataset demonstrate the accuracy and robustness of our proposed method. The code is available at https://github.com/OpenCalib/CalibAnything.

Accurate and reliable sensor calibration is critical for fusing LiDAR and inertial measurements in autonomous driving. This paper proposes a novel three-stage extrinsic calibration method between LiDAR and GNSS/INS for autonomous driving. The first stage can quickly calibrate the extrinsic parameters between the sensors through point cloud surface features so that the extrinsic can be narrowed from a large initial error to a small error range in little time. The second stage can further calibrate the extrinsic parameters based on LiDAR-mapping space occupancy while removing motion distortion. In the final stage, the z-axis (the vertical direction relative to the ground plane) errors caused by the plane motion of the autonomous vehicle are corrected, and an accurate extrinsic parameter is finally obtained. Specifically, This method utilizes the planar features in the environment, making it possible to quickly carry out calibration. Experimental results on real-world datasets demonstrate the reliability and accuracy of our method. The codes are open-sourced on the Github website. The code link is https://github.com/OpenCalib/LiDAR2INS.

Data augmentation methods to enhance perception performance in adverse weather have recently attracted considerable attention. Most of the LiDAR data augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in existing datasets, it is challenging to edit the scene and expand the diversity of traffic flow and scenario. To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather, enhancing the performance of the perception model. We complete the modeling task of the rainy weather effect in the CARLA simulator and establish a data collection pipeline for LiDAR. Furthermore, we pay special attention to the spray generated by vehicles in rainy weather and simulate this phenomenon through the Spray Emitter method we developed. In addition, considering the influence of different weather conditions on point cloud intensity, we develop a prediction network to forecast the intensity of the LiDAR echo. This enables us to complete the rainy weather simulation of 4D point cloud data. In the experiment, we observe that the model augmented by our synthetic dataset improves the performance for 3D object detection in rainy weather. Both code and dataset are available at https://github.com/PJLab-ADG/PCSim#rainypcsim.

Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role. For the calibration of LiDAR and camera, the existing method is generally to calibrate the intrinsic of the camera first and then calibrate the extrinsic of the LiDAR and camera. If the camera's intrinsic is not calibrated correctly in the first stage, it is not easy to calibrate the LiDAR-camera extrinsic accurately. Due to the complex internal structure of the camera and the lack of an effective quantitative evaluation method for the camera's intrinsic calibration, in the actual calibration, the accuracy of extrinsic parameter calibration is often reduced due to the tiny error of the camera's intrinsic parameters. To this end, we propose a novel target-based joint calibration method of the camera intrinsic and LiDAR-camera extrinsic parameters. Firstly, we design a novel calibration board pattern, adding four circular holes around the checkerboard for locating the LiDAR pose. Subsequently, a cost function defined under the reprojection constraints of the checkerboard and circular holes features is designed to solve the camera's intrinsic parameters, distortion factor, and LiDAR-camera extrinsic parameter. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robust performance. The open-source code is available at https://github.com/OpenCalib/JointCalib.

Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatic multi-LiDAR Calibration and Refinement methOd in rOad sceNe), a two-stage method including rough and refinement calibration. The first stage can calibrate the sensor from an arbitrary initial pose, and the second stage is able to precisely calibrate the sensor iteratively. Specifically, CROON utilize the nature characteristics of road scene so that it is independent and easy to apply in large-scale conditions. Experimental results on real-world and simulated data sets demonstrate the reliability and accuracy of our method. All the related data sets and codes are open-sourced on the Github website https://github.com/OpenCalib/LiDAR2LiDAR.

Specifying precise relationships among graphic elements is often a time-consuming process with traditional alignment tools. Automatic beautification of roughly designed layouts can provide a more efficient solution but often lead to undesired results due to ambiguity problems. To facilitate ambiguity resolution in layout beautification, we present a novel user interface for visualizing and editing inferred relationships through an automatic global layout beautification process. First, our interface provides a preview of the beautified layout with inferred constraints without directly modifying an input layout. In this way, the user can easily keep refining beautification results by interactively repositioning and/or resizing elements in the input layout. Second, we present a gestural interface for editing automatically inferred constraints by directly interacting with the visualized constraints via simple gestures. Our technique is applicable to both 2D and 3D global layout beautification, supported by efficient system implementation that provides instant user feedback. Our user study validates that our tool is capable of creating, editing, and refining layouts of graphic elements, and is significantly faster than the standard snap-dragging or command-based alignment tools for both 2D and 3D layout tasks.

Autonomous reconstruction of unknown scenes by a mobile robot inherently poses the question of balancing between exploration efficacy and reconstruction quality. We present a navigation-by-reconstruction approach to address this question, where moving paths of the robot are planned to account for both global efficiency for fast exploration and local smoothness to obtain high-quality scans. An RGB-D camera, attached to the robot arm, is dictated by the desired reconstruction quality as well as the movement of the robot itself. Our key idea is to harness a time-varying tensor field to guide robot movement, and then solve for 3D camera control under the constraint of the 2D robot moving path. The tensor field is updated in real time, conforming to the progressively reconstructed scene. We show that tensor fields are well suited for guiding autonomous scanning for two reasons: first, they contain sparse and controllable singularities that allow generating a locally smooth robot path, and second, their topological structure can be used for globally efficient path routing within a partially reconstructed scene. We have conducted numerous tests with a mobile robot, and demonstrate that our method leads to a smooth exploration and high-quality reconstruction of unknown indoor scenes.