Pub Date : 2024-07-26DOI: 10.1177/02783649241262333
Anup Teejo Mathew, Daniel Feliu-Talegon, Abdulaziz Y Alkayas, Frederic Boyer, Federico Renda
The need for fast and accurate analysis of soft robots calls for reduced order models (ROM). Among these, the relative reduction of strain-based ROMs follows the discretization of the strain to capture the configurations of the robot. Based on the geometrically exact variable strain parametrization of the Cosserat rod, we developed a ROM that necessitates a minimal number of degrees of freedom to represent the state of the robot: the Geometric Variable Strain (GVS) model. This model allows the static and dynamic analysis of open-, branched-, or closed-chain soft-rigid hybrid robots, all under the same mathematical framework. This paper presents for the first time the complete GVS modeling framework for a generic hybrid soft-rigid robot. Based on the Magnus expansion of the variable strain field, we developed an efficient recursive algorithm for computing the Lagrangian dynamics of the system. To discretize the soft link, we introduce state- and time-dependent basis, which is the most general form of strain basis. We classify the independent bases into global and local bases. We propose “FEM-like” local strain bases with nodal values as their generalized coordinates. Finally, using four real-world applications, we illustrate the potential of the model developed. We think that the soft robotics community will use the comprehensive framework presented in this work to analyze a wide range of specific robotic systems.
{"title":"Reduced order modeling of hybrid soft-rigid robots using global, local, and state-dependent strain parameterization","authors":"Anup Teejo Mathew, Daniel Feliu-Talegon, Abdulaziz Y Alkayas, Frederic Boyer, Federico Renda","doi":"10.1177/02783649241262333","DOIUrl":"https://doi.org/10.1177/02783649241262333","url":null,"abstract":"The need for fast and accurate analysis of soft robots calls for reduced order models (ROM). Among these, the relative reduction of strain-based ROMs follows the discretization of the strain to capture the configurations of the robot. Based on the geometrically exact variable strain parametrization of the Cosserat rod, we developed a ROM that necessitates a minimal number of degrees of freedom to represent the state of the robot: the Geometric Variable Strain (GVS) model. This model allows the static and dynamic analysis of open-, branched-, or closed-chain soft-rigid hybrid robots, all under the same mathematical framework. This paper presents for the first time the complete GVS modeling framework for a generic hybrid soft-rigid robot. Based on the Magnus expansion of the variable strain field, we developed an efficient recursive algorithm for computing the Lagrangian dynamics of the system. To discretize the soft link, we introduce state- and time-dependent basis, which is the most general form of strain basis. We classify the independent bases into global and local bases. We propose “FEM-like” local strain bases with nodal values as their generalized coordinates. Finally, using four real-world applications, we illustrate the potential of the model developed. We think that the soft robotics community will use the comprehensive framework presented in this work to analyze a wide range of specific robotic systems.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1177/02783649241258215
Patrick M. Wensing, Günter Niemeyer, Jean-Jacques E. Slotine
This paper presents an algorithm to geometrically characterize inertial parameter identifiability for an articulated robot. The geometric approach tests identifiability across the infinite space of configurations using only a finite set of conditions and without approximation. It can be applied to general open-chain kinematic trees ranging from industrial manipulators to legged robots, and it is the first solution for this broad set of systems that is provably correct. The high-level operation of the algorithm is based on a key observation: Undetectable changes in inertial parameters can be represented as sequences of inertial transfers across the joints. Drawing on the exponential parameterization of rigid-body kinematics, undetectable inertial transfers are analyzed in terms of observability from linear systems theory. This analysis can be applied recursively, and lends an overall complexity of O( N) to characterize parameter identifiability for a system of N bodies. Matlab source code for the new algorithm is provided.
本文介绍了一种从几何角度描述铰接式机器人惯性参数可识别性的算法。几何方法仅使用一组有限条件，无需近似，即可测试无限配置空间中的可识别性。该方法可应用于从工业机械手到腿部机器人的一般开链运动学树，并且是首个可证明正确性的广泛系统解决方案。该算法的高级运行基于一个关键观察结果：无法察觉的惯性参数变化可以表示为跨关节的惯性转移序列。利用刚体运动学的指数参数化，从线性系统理论的可观测性角度对不可检测的惯性转移进行了分析。这种分析可以递归应用，并以 O( N) 的总体复杂度来描述由 N 个体组成的系统的参数可识别性。本文提供了新算法的 Matlab 源代码。
{"title":"A geometric characterization of observability in inertial parameter identification","authors":"Patrick M. Wensing, Günter Niemeyer, Jean-Jacques E. Slotine","doi":"10.1177/02783649241258215","DOIUrl":"https://doi.org/10.1177/02783649241258215","url":null,"abstract":"This paper presents an algorithm to geometrically characterize inertial parameter identifiability for an articulated robot. The geometric approach tests identifiability across the infinite space of configurations using only a finite set of conditions and without approximation. It can be applied to general open-chain kinematic trees ranging from industrial manipulators to legged robots, and it is the first solution for this broad set of systems that is provably correct. The high-level operation of the algorithm is based on a key observation: Undetectable changes in inertial parameters can be represented as sequences of inertial transfers across the joints. Drawing on the exponential parameterization of rigid-body kinematics, undetectable inertial transfers are analyzed in terms of observability from linear systems theory. This analysis can be applied recursively, and lends an overall complexity of O( N) to characterize parameter identifiability for a system of N bodies. Matlab source code for the new algorithm is provided.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1177/02783649241262452
Nathaniel Merrill, Patrick Geneva, Saimouli Katragadda, Chuchu Chen, Guoquan Huang
In monocular visual-inertial navigation, it is desirable to initialize the system as quickly and robustly as possible. A state-of-the-art initialization method typically constructs a linear system to find a closed-form solution using the image features and inertial measurements and then refines the states with a nonlinear optimization. These methods generally require a few seconds of data, which however can be expedited (less than a second) by adding constraints from a robust but only up-to-scale monocular depth network in the nonlinear optimization. To further accelerate this process, in this work, we leverage the scale-less depth measurements instead in the linear initialization step that is performed prior to the nonlinear one, which only requires a single depth image for the first frame. Importantly, we show that the typical estimation of all feature states independently in the closed-form solution can be modeled as estimating only the scale and bias parameters of the learned depth map. As such, our formulation enables building a smaller minimal problem than the state of the art, which can be seamlessly integrated into RANSAC for robust estimation. Experiments show that our method has state-of-the-art initialization performance in simulation as well as on popular real-world datasets (TUM-VI, and EuRoC MAV). For the TUM-VI dataset in simulation as well as real-world, we demonstrate the superior initialization performance with only a 0.3 s window of data, which is the smallest ever reported, and validate that our method can initialize more often, robustly, and accurately in different challenging scenarios.
{"title":"Fast and robust learned single-view depth-aided monocular visual-inertial initialization","authors":"Nathaniel Merrill, Patrick Geneva, Saimouli Katragadda, Chuchu Chen, Guoquan Huang","doi":"10.1177/02783649241262452","DOIUrl":"https://doi.org/10.1177/02783649241262452","url":null,"abstract":"In monocular visual-inertial navigation, it is desirable to initialize the system as quickly and robustly as possible. A state-of-the-art initialization method typically constructs a linear system to find a closed-form solution using the image features and inertial measurements and then refines the states with a nonlinear optimization. These methods generally require a few seconds of data, which however can be expedited (less than a second) by adding constraints from a robust but only up-to-scale monocular depth network in the nonlinear optimization. To further accelerate this process, in this work, we leverage the scale-less depth measurements instead in the linear initialization step that is performed prior to the nonlinear one, which only requires a single depth image for the first frame. Importantly, we show that the typical estimation of all feature states independently in the closed-form solution can be modeled as estimating only the scale and bias parameters of the learned depth map. As such, our formulation enables building a smaller minimal problem than the state of the art, which can be seamlessly integrated into RANSAC for robust estimation. Experiments show that our method has state-of-the-art initialization performance in simulation as well as on popular real-world datasets (TUM-VI, and EuRoC MAV). For the TUM-VI dataset in simulation as well as real-world, we demonstrate the superior initialization performance with only a 0.3 s window of data, which is the smallest ever reported, and validate that our method can initialize more often, robustly, and accurately in different challenging scenarios.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.1177/02783649241264844
Michael Brockdorff, Tomas da Veiga, Joshua Davy, Peter Lloyd, James H Chandler, Giovanni Pittiglio, Ryan K Mathew, Pietro Valdastri
Independent robotic manipulation of two large permanent magnets, in the form of the dual External Permanent Magnet (dEPM) system has demonstrated the possibility for enhanced magnetic control by allowing for actuation up to eight magnetic degrees of freedom (DOFs) at clinically relevant scales. This precise off-board control has facilitated the use of magnetic agents as medical devices, including catheter-like soft continuum robots (SCRs). The use of multiple robotically actuated permanent magnets poses the risk of collision between the robotic arms, the environment, and the patient. Furthermore, unconstrained transitions between actuation inputs can lead to undesired spikes in magnetic fields potentially resulting in unsafe manipulator deformation. This paper presents a hybrid approach to trajectory planning for the dEPM platform. This is performed by splitting the planning problem in two: first finding a collision-free physical path for the two robotically actuated permanent magnets before combining this with a path in magnetic space, which permits for a smooth change in magnetic fields and gradients. This algorithm was characterized by actuating each of the eight magnetic DOFs sequentially, eliminating any potential collisions and reducing the maximum undesired actuation value by 203.7 mT for fields and by 418.7 mT/m for gradients. The effect of this planned magnetic field actuation on a SCR was then examined through two case studies. First, a tip-driven SCR was moved to set points within a confined area. Actuation using the proposed planner reduced movement outside the restricted area by an average of 41.3%. Lastly, the use of the proposed magnetic planner was shown to be essential in navigating a multi-segment magnetic SCR to the site of an aneurysm within a silicone brain phantom.
{"title":"Hybrid trajectory planning of two permanent magnets for medical robotic applications","authors":"Michael Brockdorff, Tomas da Veiga, Joshua Davy, Peter Lloyd, James H Chandler, Giovanni Pittiglio, Ryan K Mathew, Pietro Valdastri","doi":"10.1177/02783649241264844","DOIUrl":"https://doi.org/10.1177/02783649241264844","url":null,"abstract":"Independent robotic manipulation of two large permanent magnets, in the form of the dual External Permanent Magnet (dEPM) system has demonstrated the possibility for enhanced magnetic control by allowing for actuation up to eight magnetic degrees of freedom (DOFs) at clinically relevant scales. This precise off-board control has facilitated the use of magnetic agents as medical devices, including catheter-like soft continuum robots (SCRs). The use of multiple robotically actuated permanent magnets poses the risk of collision between the robotic arms, the environment, and the patient. Furthermore, unconstrained transitions between actuation inputs can lead to undesired spikes in magnetic fields potentially resulting in unsafe manipulator deformation. This paper presents a hybrid approach to trajectory planning for the dEPM platform. This is performed by splitting the planning problem in two: first finding a collision-free physical path for the two robotically actuated permanent magnets before combining this with a path in magnetic space, which permits for a smooth change in magnetic fields and gradients. This algorithm was characterized by actuating each of the eight magnetic DOFs sequentially, eliminating any potential collisions and reducing the maximum undesired actuation value by 203.7 mT for fields and by 418.7 mT/m for gradients. The effect of this planned magnetic field actuation on a SCR was then examined through two case studies. First, a tip-driven SCR was moved to set points within a confined area. Actuation using the proposed planner reduced movement outside the restricted area by an average of 41.3%. Lastly, the use of the proposed magnetic planner was shown to be essential in navigating a multi-segment magnetic SCR to the site of an aneurysm within a silicone brain phantom.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-24DOI: 10.1177/02783649241249194
Sami Haddadin, Erfan Shahriari
Unified force-impedance control (UFIC) aims at integrating the advantages of impedance control and force control. Compliance and exact force regulation are equally important abilities in modern robot manipulation. The developed passivity-based framework builds on the energy tank concept and is suitable for serial rigid and flexible-joint robots. Furthermore, it is able to deal either with direct force measurements or model-based contact force estimation. Thus, in this theoretical framework, the most relevant practical systems are covered and shown to be stable for arbitrary passive environments. Particular focus is also laid on a robust impedance-based contact/non-contact stabilization methodology that prevents abrupt, unwanted, and potentially dangerous movements of the manipulator in case of contact loss, a well-known problem of both impedance and force control. The validity of the approach is shown in simulation and through various experiments. Our work roots in Haddadin (2015); Schindlbeck and Haddadin (2015), where the basic UFIC regulation controller was proposed. In the present paper, we significantly advance this idea into a complete theoretical UFIC tracking framework, including rigorous stability analysis and extensive experimental evidence.
{"title":"Unified force-impedance control","authors":"Sami Haddadin, Erfan Shahriari","doi":"10.1177/02783649241249194","DOIUrl":"https://doi.org/10.1177/02783649241249194","url":null,"abstract":"Unified force-impedance control (UFIC) aims at integrating the advantages of impedance control and force control. Compliance and exact force regulation are equally important abilities in modern robot manipulation. The developed passivity-based framework builds on the energy tank concept and is suitable for serial rigid and flexible-joint robots. Furthermore, it is able to deal either with direct force measurements or model-based contact force estimation. Thus, in this theoretical framework, the most relevant practical systems are covered and shown to be stable for arbitrary passive environments. Particular focus is also laid on a robust impedance-based contact/non-contact stabilization methodology that prevents abrupt, unwanted, and potentially dangerous movements of the manipulator in case of contact loss, a well-known problem of both impedance and force control. The validity of the approach is shown in simulation and through various experiments. Our work roots in Haddadin (2015); Schindlbeck and Haddadin (2015), where the basic UFIC regulation controller was proposed. In the present paper, we significantly advance this idea into a complete theoretical UFIC tracking framework, including rigorous stability analysis and extensive experimental evidence.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.1177/02783649241263114
Ziyou Wu, Dan Zhao, Shai Revzen
Multi-legged robots with six or more legs are not in common use, despite designs with superior stability, maneuverability, and a low number of actuators being available for over 20 years. This may be in part due to the difficulty in modeling multi-legged motion with slipping and producing reliable predictions of body velocity. Here, we present a detailed measurement of the foot contact forces in a hexapedal robot with multiple sliding contacts, and provide an algorithm for predicting these contact forces and the body velocity. The algorithm relies on the recently published observation that even while slipping, multi-legged robots are principally kinematic, and employ a friction law ansatz that allows us to compute the shape-change to body-velocity connection and the foot contact forces. This results in the ability to simulate motion plans for a large number of contacts, each potentially with slipping. Furthermore, in homogeneous environments, this kind of simulation can run in (parallel) logarithmic time of the planning horizon.
{"title":"Modeling multi-legged robot locomotion with slipping and its experimental validation","authors":"Ziyou Wu, Dan Zhao, Shai Revzen","doi":"10.1177/02783649241263114","DOIUrl":"https://doi.org/10.1177/02783649241263114","url":null,"abstract":"Multi-legged robots with six or more legs are not in common use, despite designs with superior stability, maneuverability, and a low number of actuators being available for over 20 years. This may be in part due to the difficulty in modeling multi-legged motion with slipping and producing reliable predictions of body velocity. Here, we present a detailed measurement of the foot contact forces in a hexapedal robot with multiple sliding contacts, and provide an algorithm for predicting these contact forces and the body velocity. The algorithm relies on the recently published observation that even while slipping, multi-legged robots are principally kinematic, and employ a friction law ansatz that allows us to compute the shape-change to body-velocity connection and the foot contact forces. This results in the ability to simulate motion plans for a large number of contacts, each potentially with slipping. Furthermore, in homogeneous environments, this kind of simulation can run in (parallel) logarithmic time of the planning horizon.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1177/02783649241260428
Daegyu Lim, Myeong-Ju Kim, Junhyeok Cha, Jaeheung Park
Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB, substantially reducing the estimation errors of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Furthermore, MOB-Net shows more robust performance for the unseen data compared to the end-to-end learning method, and the robustness of MOB-Net is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented to show the versatility of MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.
{"title":"MOB-Net: Limb-modularized uncertainty torque learning of humanoids for sensorless external torque estimation","authors":"Daegyu Lim, Myeong-Ju Kim, Junhyeok Cha, Jaeheung Park","doi":"10.1177/02783649241260428","DOIUrl":"https://doi.org/10.1177/02783649241260428","url":null,"abstract":"Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB, substantially reducing the estimation errors of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Furthermore, MOB-Net shows more robust performance for the unseen data compared to the end-to-end learning method, and the robustness of MOB-Net is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented to show the versatility of MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-21DOI: 10.1177/02783649241266853
Advaith V. Sethuraman, Anja Sheppard, Onur Bagoren, Christopher Pinnow, Jamey Anderson, Timothy C. Havens, Katherine A. Skinner
Open-source benchmark datasets have been a critical component for advancing machine learning for robot perception in terrestrial applications. Benchmark datasets enable the widespread development of state-of-the-art machine learning methods, which require large datasets for training, validation, and thorough comparison to competing approaches. Underwater environments impose several operational challenges that hinder efforts to collect large benchmark datasets for marine robot perception. Furthermore, a low abundance of targets of interest relative to the size of the search space leads to increased time and cost required to collect useful datasets for a specific task. As a result, there is limited availability of labeled benchmark datasets for underwater applications. We present the AI4Shipwrecks dataset, which consists of 28 distinct shipwrecks totaling 286 high-resolution labeled side scan sonar images to advance the state-of-the-art in autonomous sonar image understanding. We leverage the unique abundance of targets in Thunder Bay National Marine Sanctuary in Lake Huron, MI, to collect and compile a sonar imagery benchmark dataset through surveys with an autonomous underwater vehicle (AUV). We consulted with expert marine archaeologists for the labeling of robotically gathered data. We then leverage this dataset to perform benchmark experiments for comparison of state-of-the-art supervised segmentation methods, and we present insights on opportunities and open challenges for the field. The dataset and benchmarking tools will be released as an open-source benchmark dataset to spur innovation in machine learning for Great Lakes and ocean exploration. The dataset and accompanying software are available at https://umfieldrobotics.github.io/ai4shipwrecks/ .
{"title":"Machine learning for shipwreck segmentation from side scan sonar imagery: Dataset and benchmark","authors":"Advaith V. Sethuraman, Anja Sheppard, Onur Bagoren, Christopher Pinnow, Jamey Anderson, Timothy C. Havens, Katherine A. Skinner","doi":"10.1177/02783649241266853","DOIUrl":"https://doi.org/10.1177/02783649241266853","url":null,"abstract":"Open-source benchmark datasets have been a critical component for advancing machine learning for robot perception in terrestrial applications. Benchmark datasets enable the widespread development of state-of-the-art machine learning methods, which require large datasets for training, validation, and thorough comparison to competing approaches. Underwater environments impose several operational challenges that hinder efforts to collect large benchmark datasets for marine robot perception. Furthermore, a low abundance of targets of interest relative to the size of the search space leads to increased time and cost required to collect useful datasets for a specific task. As a result, there is limited availability of labeled benchmark datasets for underwater applications. We present the AI4Shipwrecks dataset, which consists of 28 distinct shipwrecks totaling 286 high-resolution labeled side scan sonar images to advance the state-of-the-art in autonomous sonar image understanding. We leverage the unique abundance of targets in Thunder Bay National Marine Sanctuary in Lake Huron, MI, to collect and compile a sonar imagery benchmark dataset through surveys with an autonomous underwater vehicle (AUV). We consulted with expert marine archaeologists for the labeling of robotically gathered data. We then leverage this dataset to perform benchmark experiments for comparison of state-of-the-art supervised segmentation methods, and we present insights on opportunities and open challenges for the field. The dataset and benchmarking tools will be released as an open-source benchmark dataset to spur innovation in machine learning for Great Lakes and ocean exploration. The dataset and accompanying software are available at https://umfieldrobotics.github.io/ai4shipwrecks/ .","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1177/02783649241261079
Yiujia Zhang, SeyedMostafa Ahmadi, Jungwon Kang, Zahra Arjmandi, Gunho Sohn
The York University Teledyne Optech (YUTO) Mobile Mapping System (MMS) Dataset, encompassing four sequences totaling 20.1 km, was thoroughly assembled through two data collection expeditions on August 12, 2020, and June 21, 2019. Acquisitions were performed using a uniquely equipped vehicle, fortified with a panoramic camera, a tilted LiDAR, a Global Positioning System (GPS), and an Inertial Measurement Unit (IMU), journeying through two strategic locations: the York University Keele Campus in Toronto and the Teledyne Optech headquarters in City of Vaughan, Canada. This paper not only delineates the comprehensive overview of the YUTO MMS dataset, delving into aspects such as the collection procedure, sensor configuration, synchronization, data structure and format but also presents a robust benchmark of prevailing Simultaneous Localization and Mapping (SLAM) systems. By subjecting them to analysis utilizing the introduced dataset, this research lays a foundational baseline for ensuing studies, thereby contributing to advancements and enhancements in the SLAM-integrated mobile mapping system. The dataset can be downloaded from: https://ausmlab.github.io/yutomms/ .
{"title":"YUTO MMS: A comprehensive SLAM dataset for urban mobile mapping with tilted LiDAR and panoramic camera integration","authors":"Yiujia Zhang, SeyedMostafa Ahmadi, Jungwon Kang, Zahra Arjmandi, Gunho Sohn","doi":"10.1177/02783649241261079","DOIUrl":"https://doi.org/10.1177/02783649241261079","url":null,"abstract":"The York University Teledyne Optech (YUTO) Mobile Mapping System (MMS) Dataset, encompassing four sequences totaling 20.1 km, was thoroughly assembled through two data collection expeditions on August 12, 2020, and June 21, 2019. Acquisitions were performed using a uniquely equipped vehicle, fortified with a panoramic camera, a tilted LiDAR, a Global Positioning System (GPS), and an Inertial Measurement Unit (IMU), journeying through two strategic locations: the York University Keele Campus in Toronto and the Teledyne Optech headquarters in City of Vaughan, Canada. This paper not only delineates the comprehensive overview of the YUTO MMS dataset, delving into aspects such as the collection procedure, sensor configuration, synchronization, data structure and format but also presents a robust benchmark of prevailing Simultaneous Localization and Mapping (SLAM) systems. By subjecting them to analysis utilizing the introduced dataset, this research lays a foundational baseline for ensuing studies, thereby contributing to advancements and enhancements in the SLAM-integrated mobile mapping system. The dataset can be downloaded from: https://ausmlab.github.io/yutomms/ .","PeriodicalId":501362,"journal":{"name":"The International Journal of Robotics Research","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1177/02783649241256970
Brendon Forsgren, Michael Kaess, Ram Vasudevan, Timothy W. McLain, Joshua G. Mangelson
This paper unifies the theory of consistent-set maximization for robust outlier detection in a simultaneous localization and mapping framework. We first describe the notion of pairwise consistency before discussing how a consistency graph can be formed by evaluating pairs of measurements for consistency. Finding the largest set of consistent measurements is transformed into an instance of the maximum clique problem and can be solved relatively quickly using existing maximum-clique solvers. We then generalize our algorithm to check consistency on a group- k basis by using a generalized notion of consistency and using generalized graphs. We also present modified maximum clique algorithms that function over generalized graphs to find the set of measurements that is internally group- k consistent. We address the exponential nature of group- k consistency and present methods that can substantially decrease the number of necessary checks performed when evaluating consistency. We extend our prior work to perform data association, and to multi-agent systems in both simulation and hardware, and provide a comparison with other state-of-the-art methods.
本文将一致集最大化理论统一到同时定位和映射的框架中，用于稳健的离群点检测。我们首先描述了成对一致性的概念，然后讨论了如何通过评估测量值对的一致性来形成一致性图。寻找最大的一致性测量数据集被转化为最大簇问题的一个实例，并可使用现有的最大簇求解器相对快速地求解。然后，我们通过使用广义的一致性概念和广义图，将算法推广到以 k 组为基础检查一致性。我们还提出了在广义图上运行的修正最大簇算法，以找到内部 k 组一致的测量集。我们解决了 k 组一致性的指数性质问题，并提出了在评估一致性时可大幅减少必要检查次数的方法。我们将先前的工作扩展到了数据关联以及模拟和硬件中的多代理系统，并提供了与其他最先进方法的比较。
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