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3D Mapping for a Large Crane Using Rotating 2D-Lidar and IMU Attached to the Crane Boom

IEEE ACCESS 2023年 11卷 21104-21116页

作者： Ul Hassan, Mahmood Das, Dipankar Miura, Jun Toyohashi Univ Technol Dept Comp Sci & Engn Toyohashi Japan Univ Rajshahi Dept Informat & Commun Engn B-6205 Rajshahi Bangladesh

This paper describes a novel method for large-scale 3D mapping for construction cranes with an arbitrary motion of the sensor system (2D lidar and IMU) attached to the crane boom. A heavy lidar with a slowly rotating base is needed to make a large-scale map both vertically and horizontally for cranes. This sensor configuration and mapping conditions entail handling each 2D scan separately, making it difficult to adopt existing rotating 2D lidar-based methods that construct a virtual 3D scan from a set of 2D scans. In the proposed method, we introduce a complementary filter with moving average filtering for lidar pose estimation, which is more robust to severe vibration than Kalman filter-based methods. As there are only a small amount of overlaps between 2D lidar scans, we propose a map correction method based on a pose graph optimization with planar environmental constraints. We evaluate the proposed method in a simulation and a small-sized real environment and compare it with one of the state-of-the-art methods. The evaluation results reveal that the proposed method can accurately estimate the sensor poses, thereby generating a high-quality, large-scale 3D point cloud map.

关键词： Laser radar Cranes Three-dimensional displays Vibrations Magnetometers Distortion Quaternions optimization 3D mapping 2D lidar IMU pose graph optimization complementary filter large crane

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Targetless Extrinsic Calibration of Camera and Low-Resolution 3-D LiDAR

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IEEE SENSORS JOURNAL 2023年第10期23卷 10889-10899页

作者： Ou, Ni Cai, Hanyu Yang, Jiawen Wang, Junzheng Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

Autonomous driving heavily relies on light detection and ranging (LiDAR) and camera sensors, which can significantly improve the performance of perception and navigation tasks when fused together. The success of this cross-modality fusion hinges on accurate extrinsic calibration. In recent years, targetless LiDAR-camera calibration methods have gained increasing attention, thanks to their independence from external targets. Nevertheless, developing a targetless method for low-resolution LiDARs remains challenging due to the difficulty in extracting reliable features from point clouds with limited LiDAR beams. In this article, we propose a robust targetless method to solve this struggling problem. It can automatically estimate accurate LiDAR and camera poses and solve the extrinsic matrix through hand-eye calibration. Moreover, we also carefully analyze pose estimation issues existing in the low-resolution LiDAR and present our solution. Real-world experiments are carried out on an unmanned ground vehicle (UGV)-mounted multisensor platform containing a charged-coupled device (CCD) camera and a VLP-16 LiDAR. For evaluation, we use a state-of-the-art target-based calibration approach to generate the ground truth extrinsic parameters. Experimental results demonstrate that our method achieves low calibration error in both translation (3 cm) and rotation (0.59 degrees).

关键词： Camera light detection and ranging (LiDAR) pose graph optimization sensor calibration simultaneous localization and mapping (SLAM)

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Distributed UAV Swarm Collaborative SLAM Based on Visual-Inertial-Ranging Measurement

Distributed UAV Swarm Collaborative SLAM Based on Visual-Ine...

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2024 International Conference on Guidance, Navigation and Control

作者： Shou, Yicheng Song, Zhuoyue Qi, Haoyu Li, Zhen Chen, Wenjie Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

ISBN: (纸本)9789819622221;9789819622207;9789819622191

Compared with the standalone operation, unmanned aerial vehicle (UAV) swarm systems bring the high efficiency to the task execution in a cooperative manner, and they has gained the widespread application in disaster relief, power inspection and other fields. To support the fulfillment of cooperative task, the cooperative positioning capability plays the key role in global positioning system (GPS) denied areas. However, there are still difficulties in initialization, high communication demand, and inevitable drift for swarm cooperative positioning. In order to solve the problems above, a distributed collaborative simultaneous localization and mapping (SLAM) system is developed based on ranging measurement from ultra-wideband (UWB), which specifically accelerates the initialization by introducing prior information, and optimizes the communication framework between the UAVs so as to run in real time. Besides, the ranging is introduced as the restraints on the relative drift between the nodes. The evaluation are performed on datasets recorded in a lab environment. Experiments results show that our method surpasses 15% over the mainstream visual-inertial odometry in positioning accuracy. Furthermore, our method effectively reduces the drift of the visual-inertial odometry in challenging scenarios on visual.

关键词： collaborative SLAM multi-robot system pose graph optimization visual-inertial-ranging SLAM

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A Submodular-Based Autonomous Exploration for Multi-Room Indoor Scenes Reconstruction 40th

A Submodular-Based Autonomous Exploration for Multi-Room Ind...

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40th Computer graphics International Conference (CGI)

作者： Miao, Yongwei Wang, Haipeng Fan, Ran Liu, Fuchang Zhejiang Sci Tech Univ Coll Mech Engn Hangzhou Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9783031500718;9783031500725

To autonomously explore and densely recover an unknown indoor scene is a nontrivial task in 3D scene reconstruction. It is challenging for scenes composed of compact and complicated interconnected rooms with no priors. To address this issue, we aim to use autonomous scanning, reconstruct multi-room scenes, and produce a complete reconstruction in as few scans as possible. With a progressive discrete motion planning module, we introduce submodular-based planning for automated scanning scenarios to efficiently guide the active scanning by Next-Best-View until marginal gains diminish. The submodular-based planning gives an approximately optimal solution of "Next-Best-View" which is NP-hard in case of no prior knowledge. Experiments show that our method can improve scanning efficiency significantly for multi-room scenes while maintaining reconstruction errors.

关键词： Autonomous Exploration Indoor Scenes Reconstruction Submodular Function pose graph optimization

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Loopy-SLAM: Dense Neural SLAM with Loop Closures

Loopy-SLAM: Dense Neural SLAM with Loop Closures

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liso, Lorenzo Sandstrom, Erik Yugay, Vladimir Van Gool, Luc Oswald, Martin R. Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Amsterdam Amsterdam Netherlands INSAIT Sofia Bulgaria

ISBN: (纸本)9798350353006

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response, we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Robust pose graph optimization is used to rigidly align the local submaps. As our representation is point based, map corrections can be performed efficiently without the need to store the entire history of input frames used for mapping as typically required by methods employing a grid based mapping structure. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking, mapping, and rendering accuracy when compared to existing dense neural RGBD SLAM methods. Project page: ***/Loopy-SLAM/.

关键词： dense localization loop closure mapping pose graph optimization SLAM tracking

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Accurate Registration of Transparent Objects in 2D LiDAR SLAM 15th

Accurate Registration of Transparent Objects in 2D LiDAR SLA...

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15th European Robotics Forum

作者： Herr, Gurtajbir Singh Weerakoon, Lasitha Chopra, Nikhil Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (纸本)9783031764264;9783031764240;9783031764233

Transparent objects and surfaces present a unique challenge to the application of LiDAR for indoor mobile robotics. This is due to the fact that the majority of the incident light (except for normal incidence) passes through such objects and results in them becoming "invisible" to the resulting map. In this work, we exploit the sharp intensity spikes that occur when an incident beam strikes a transparent surface perpendicularly, and keep an accurate account of such points. This enables us to make the representation unaffected by the drift accumulated as the robot moves through the environment. Our method utilizes the pose graph optimization (PGO) framework and exhibits superior results when compared to an existing approach that utilizes intensity spikes for detecting transparent surfaces but uses a particle filter based approach for generating the map of the environment. We showcase how, by leveraging the inherent structure of a PGO framework, the transparent objects can be mapped accurately.

关键词： LiDAR SLAM pose graph optimization

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Distributed Trajectory Estimation for Multi-Autonomous Underwater Vehicle

Distributed Trajectory Estimation for Multi-Autonomous Under...

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OCEANS Conference

作者： Feng, Haihan Chu, Shuguang Lin, Mingwei Li, Dejun Zhejiang Univ Sch Mech Engn Hangzhou Peoples R China

ISBN: (纸本)9798350362077

Multi-AUV collaboration is significant in marine resource exploration, submarine scientific observation and underwater rescue. However, limited communication bandwidth and acoustic range challenge trajectory estimation of AUVs. In this paper, we design a distributed multi-AUV system and implement an optimizer based on the Alternating Direction Method of Multipliers (ADMM). In our system, each AUV is required to meet and exchange information with others locally. This optimizer treats measurements between AUVs as constraints and transforms the global optimization problem into multiple sub-problems, which allows each AUV to optimize its trajectory independently. Compared with a representative algorithm on the same dataset, this approach effectively reduces the optimization time and improves the convergence efficiency while ensuring convergence accuracy.

关键词： Multi-Autonomous underwater vehicle distributed trajectory estimation pose graph optimization alternating direction method of multipliers

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Real-Time Lidar Odometry and Mapping with Loop Closure

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SENSORS 2022年第12期22卷 4373-4373页

作者： Liu, Yonghui Zhang, Weimin Li, Fangxing Zuo, Zhengqing Huang, Qiang Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency.

关键词： real-time lidar odometry submap-based loop-closure detection pose graph optimization simultaneous localization and mapping (SLAM)

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Weighted multi-node joint refinement for VSLAM pose-graph robustification

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第5期36卷 056303-056303页

作者： Cai, Jie Wei, Guoliang Hu, Hongbo Univ Shanghai Sci & Technol Coll Sci Jungong Rd Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Business Sch Jungong Rd Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Jungong Rd Shanghai 200093 Peoples R China

In visual simultaneous localization and mapping (VSLAM) systems, pose-graph optimization accuracy is often compromised by noise and errors in loop closures, especially in large-scale or complex environments. This paper introduces an intermediate processor to address these challenges and proposes a novel algorithm, Weighted Multi-Node Joint Refinement for VSLAM pose-graph Robustification (WMJR-PGO). The WMJR-PGO algorithm incorporates a spatial residual mapping test to enhance loop closure detection and a weighted breadth-first search to refine pose estimation. This approach effectively mitigates front-end errors and ensures back-end consistency. Extensive experiments show that the WMJR-PGO algorithm achieves significant improvements across various noise levels. On the Tours dataset, WMJR-PGO outperforms the RS algorithm by 63 % in optimization accuracy under a noise standard deviation of sigma r=0.5. Furthermore, the WMJR-PGO algorithm demonstrates superior performance on multiple datasets even with a noise standard deviation of sigma r=0.5, proving its robustness in noisy environments. The proposed intermediate processor serves as an independent optimization module and provides efficient initialization for iterative optimization methods, accelerating convergence and improving overall system performance.

关键词： VSLAM pose graph optimization spatial residual mapping weighted breadth-first-search

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A tracker pose optimization method for robotic measuring system based on spatial distance constraints

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MEASUREMENT 2025年 243卷

作者： Lin, Xiaoyu Wang, Ziwei Yang, Yifan Qi, Bosong Zhang, Xiaojian Yan, Sijie Ding, Han Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Luo Yu Rd 1037 Wuhan 430074 Peoples R China Hust Wuxi Res Inst Yan Xin Rd 329 Wuxi 214174 Peoples R China

In large-scale metrology (LSM), the transformation of the tracker base frame (TBF) is a predominant method to enlarge the field of view (FOV) of the tracking sensor for full-field 3D measurements. Nevertheless, such a process will introduce cumulative errors and significantly diminish the global point cloud alignment accuracy. To address this problem, we propose a novel tracker pose optimization method for TBF transformation. A pose graph optimization (PGO) model based on spatial distance constraints is implemented to improve the tracker pose accuracy. We also adopt a robust coefficient and a damping factor to simplify the experimental process and stabilize the convergence results. Simulations and experiments on high-speed train surfaces are conducted to validate our method's accuracy and effectiveness. The results indicate that our optimization method outperforms two existing methods in spatial positioning accuracy and point cloud alignment accuracy, which showcases its practical applicability and superiority in manufacturing scenarios.

关键词： Tracker base frame transformation Photogrammetry tracking pose graph optimization Spatial distance constraints Large-scale metrology Full-field 3D measurement

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