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SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

引用

IEEE international conference on robotics and automation (ICRA)

作者： Li, Lin Kong, Xin Zhao, Xiangrui Li, Wanlong Wen, Feng Zhang, Hongbo Liu, Yong Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (纸本)9781728190778

LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentation for point cloud, semantic information can be obtained conveniently and steadily, essential for highlevel intelligence and conductive to SLAM. In this paper, we present a novel semantic-aided LiDAR SLAM with loop closure based on LOAM, named SA-LOAM, which leverages semantics in odometry as well as loop closure detection. Specifically, we propose a semantic-assisted ICP, including semantically matching, downsampling and plane constraint, and integrates a semantic graph-based place recognition method in our loop closure detection module. Renefitting from semantics, we can improve the localization accuracy, detect loop closures effectively, and construct a global consistent semantic map even in large-scale scenes. Extensive experiments on KITTI and Ford Campus dataset show that our system significantly improves baseline performance, has generalization ability to unseen data and achieves competitive results compared with state-of-the-art methods.

关键词： Location awareness Simultaneous localization and mapping Laser radar Three-dimensional displays automation conferences Semantics

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Safe, Passive Control for Mechanical Systems with Application to Physical Human-Robot Interactions

Safe, Passive Control for Mechanical Systems with Applicatio...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Cortez, Wenceslao Shaw Verginis, Christos K. Dimarogonas, Dimos, V Royal Inst Technol KTH Sch EECS S-10044 Stockholm Sweden

ISBN: (纸本)9781728190778

In this paper, we propose a novel safe, passive, and robust control law for mechanical systems. The proposed approach addresses safety from a physical human-robot interaction perspective, where a robot must not only stay inside a pre-defined region, but respect velocity constraints and ensure passivity with respect to external perturbations that may arise from a human or the environment. The proposed control is written in closed-form, behaves well even during singular configurations, and allows any nominal control law to be applied inside the operating region as long as the safety requirements (e.g., velocity) are adhered to. The proposed method is implemented on a 6-DOF robot to demonstrate its effectiveness during a physical human-robot interaction task.

关键词： Robust control Perturbation methods Redundancy Human-robot interaction Control systems Robustness Safety

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Visuotactile Feedback Parallel Gripper for Robotic Adaptive Grasping

Visuotactile Feedback Parallel Gripper for Robotic Adaptive ...

引用

2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Boyue Cui, Shaowei Zhang, Chaofan Hu, Jingyi Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

In robot grasp and dexterous manipulation tasks, tactile sensing is important for the control adjustment of the manipulator. In this paper, we present a novel low-cost parallel gripper with high-resolution tactile sensing, named the GelStereo Gripper. Furthermore, an adaptive grasp strategy is proposed to endow the gripper with tactile-feedback grasp stability-maintaining ability. We install the gripper on our robot platform and conduct various grasp experiments by utilizing proposed control methods. Experimental results verify the reliability of the GelStereo gripper and also prove the effectiveness of the proposed strategy for experimental objects with different features. © 2022 IEEE.

关键词： Grippers

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Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules

Skills Composition Framework for Reconfigurable Cyber-Physic...

引用

international conference on Emerging Technologies and Factory automation (ETFA)

作者： Aleksandr Sidorenko Achim Wagner Martin Ruskowski Innovative Factory System German Research Center for Artificial Intelligence Kaiserslautern Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The behavior trees (BTs) framework is a novel approach that enables the intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers. This shows the instantiation of the proposed framework with specific industrial technology to enable its evaluation by the automation community.

关键词： Adaptation models Protocols Software architecture Computational modeling Production Computer architecture Software Artificial intelligence Robots Manufacturing systems

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Efficient and Accurate Template-based Reconstruction of Deformable Surfaces

Efficient and Accurate Template-based Reconstruction of Defo...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Dominik Slomma Shoudong Huang Liang Zhao Faculty of Engineering and Information Technology Robotics Institute University of Technology Sydney Australia School of Informatics The University of Edinburgh Scotland

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

3D surface reconstruction in deformable environments presents significant challenges. Template-based methods have proven robust for achieving accurate reconstructions by utilising images and textured triangulated meshes as reference data. These methods rely on feature detection in both the reference and current images to establish corresponding points, leveraging reprojection and deformation constraints for precise reconstruction. However, challenges arise when features are not uniformly distributed across mesh triangles, potentially resulting in sparse or coarse reconstructions. Moreover, the combined computational cost of reprojection and deformable constraints often leads to prolonged optimisation times. This study aims to enhance efficiency in reconstructing deformations within the field of view. Our approach involves back-projecting vertices from a reference mesh onto the reference image plane and subsequently tracking them directly in the subsequent image. This method assumes the resulting observations are sufficiently accurate, encoding the deformation within this information. By eliminating the re-projection constraint and focusing solely on a deformation constraint based on Euclidean distances between vertices, we significantly reduce computational and memory costs. The results of our proposed algorithm demonstrate a notable reduction in computational cost and memory cost, while maintaining reconstruction accuracy comparable to related methods. The code of our algorithm is publicly available at https://***/DominikSlomma/Efficient-and-Accurate-Template-based-Reconstruction-of-Deformable-Surfaces

关键词： Surface reconstruction Accuracy Three-dimensional displays Costs Deformation Linear programming Computational efficiency Image reconstruction Optimization Robots

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Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization

Unsupervised Feature Learning for Manipulation with Contrast...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Rabinovitz, Carmel Grupen, Niko Tamar, Aviv Technion Dept Elect Engn Haifa Israel Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9781728190778

Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Recently, it has been suggested to learn such features in an unsupervised manner from simulated, self-supervised, robot interaction;the idea being that high-level physical properties are well captured by modern physical simulators, and their representation from visual inputs may transfer well to the real world. In particular, learning methods based on noise contrastive estimation have shown promising results. To robustify the simulation-to-real transfer, domain randomization (DR) was suggested for learning features that are invariant to irrelevant visual properties such as textures or lighting. In this work, however, we show that a naive application of DR to unsupervised learning based on contrastive estimation does not promote invariance, as the loss function maximizes mutual information between the features and both the relevant and irrelevant visual properties. We propose a simple modification of the contrastive loss to fix this, exploiting the fact that we can control the simulated randomization of visual properties. Our approach learns physical features that are significantly more robust to visual domain variation, as we demonstrate using both rigid and non-rigid objects.

关键词： Visualization automation conferences Estimation Lighting Robot learning Task analysis

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Online DCM Trajectory Adaptation for Push and Stumble Recovery during Humanoid Locomotion

Online DCM Trajectory Adaptation for Push and Stumble Recove...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Mesesan, George Englsberger, Johannes Ott, Christian German Aerosp Ctr DLR Inst Robot & Mechatron Wessling Germany

ISBN: (纸本)9781728190778

In this paper, we present a highly efficient Divergent Component of Motion (DCM) reference trajectory generator capable of adapting online to large perturbations acting on the center-of-mass (push recovery) and on the swing foot (stumble recovery). For push recovery, we propose an analytic solution for a footstep adjustment strategy based on the DCM dynamics. The proposed algorithm considers double support phases explicitly and is active throughout the motion, i.e., during both single and double support phases. For stumble recovery, we introduce a continuous DCM trajectory adaptation based on the instantaneous tracking error of the swing foot. Our method is highly efficient, computing a push recovery solution within 10 microseconds on the robot hardware. Furthermore, it achieves robust locomotion for large external perturbations, which we demonstrate in simulations and experiments with the humanoid robot TORO.

关键词： Tracking Perturbation methods Heuristic algorithms conferences Computational modeling Dynamics Humanoid robots

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Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration

Colmap-PCD: An Open-source Tool for Fine Image-to-point clou...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chunge Bai Ruijie Fu Xiang Gao AgiBot Technology Co. Ltd. and Tsinghua University Robotics Institute at Carnegie Mellon University AgiBot Technology Co. Ltd.

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

State-of-the-art techniques for monocular camera reconstruction predominantly rely on the Structure from Motion (SfM) pipeline. However, such methods often yield reconstruction outcomes that lack crucial scale information, and over time, accumulation of images leads to inevitable drift issues. In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches. Researchers have made attempts to utilize concurrent LiDAR and camera measurements in pursuit of precise scaling and color details within mapping outcomes. However, the outcomes are subject to extrinsic calibration and time synchronization precision. In this paper, we propose a novel cost-effective reconstruction pipeline that utilizes a pre-established LiDAR map as a fixed constraint to effectively address the inherent scale challenges present in monocular camera reconstruction. To our knowledge, our method is the first to register images onto the point cloud map without requiring synchronous capture of camera and LiDAR data, granting us the flexibility to manage reconstruction detail levels across various areas of interest. To facilitate further research in this domain, we have released Colmap-PCD 3 , an open-source tool leveraging the Colmap algorithm, that enables precise fine-scale registration of images to the point cloud map.

关键词： Point cloud compression Location awareness Laser radar Structure from motion Pipelines Cameras Synchronization

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Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

Multi-Object RANSAC: Efficient Plane Clustering Method in a ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Seunghyeon Lim Youngjae Yoo Jun Ki Lee Byoung-Tak Zhang Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea AIIS Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large- scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by postprocessing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

关键词： Point cloud compression Instance segmentation Clustering methods Robot vision systems Grasping Cameras Clutter

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Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning

Autonomous Overtaking in Gran Turismo Sport Using Curriculum...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Song, Yunlong Lin, HaoChih Kaufmann, Elia Duerr, Peter Scaramuzza, Davide Univ Zurich Dept Informat Robot & Percept Grp Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Sony AI Zurich Zurich Switzerland

ISBN: (纸本)9781728190778

Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics, and cannot leverage the large volume of data generated by simulation or real-world driving. To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem. We evaluate our approach in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks. By leveraging curriculum learning, our approach leads to faster convergence as well as increased performance compared to vanilla reinforcement learning. As a result, the trained controller outperforms the built-in model-based game AI and achieves comparable overtaking performance with an experienced human driver.

关键词： Solid modeling Reinforcement learning Games Data models Automobiles Vehicle dynamics Task analysis

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