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A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

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IFAC-PapersOnLine 2023年第2期56卷 1003-1008页

作者： Ivan Bilić Filip Marić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics University of Toronto Space and Terrestrial Autonomous Robotic Systems Laboratory

autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results.

关键词： Manipulation Mechatronic systems robotics Joint angle estimation

Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data

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Contrastive Learning for Self-Supervised Pre-Training of Poi...

Canadian Conference on Computer and Robot Vision, CRV

作者： Andrej Janda Brandon Wagstaff Edwin G. Ng Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Canada

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GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

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arXiv 2024年

作者： Bilić, Ivan Marić, Filip Bonsignorio, Fabio Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

In autonomous robotics, measurement of the robot’s internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose. Our code is available at https://***/iwhitey/GISR-robot. © 2024, CC BY.

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A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

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arXiv 2023年

作者： Bilić, Ivan Marić, Filip Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot’s kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results. © 2023, CC BY-NC-ND.

关键词： Manipulators

Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation

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arXiv 2023年

作者： Janda, Andrej Merriaux, Pierre Olivier, Pierre Kelly, Jonathan Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Toronto Canada LeddarTech Inc. Québec Canada

Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range measurements, flash lidar sensors are able to provide information about the shape of the return pulse. The shape of the return waveform is affected by many factors, including the distance that the light pulse travels and the angle of incidence with a surface. Importantly, the shape of the return waveform also depends on the material properties of the reflecting surface. In this paper, we investigate whether the material type or class can be determined from the full-waveform response. First, as a proof of concept, we demonstrate that the extra information about material class, if known accurately, can improve performance on scene understanding tasks such as semantic segmentation. Next, we learn two different full-waveform material classifiers: a random forest classifier and a temporal convolutional neural network (TCN) classifier. We find that, in some cases, material types can be distinguished, and that the TCN generally performs better across a wider range of materials. However, factors such as angle of incidence, material colour, and material similarity may hinder overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation

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Living in a Material World: Learning Material Properties fro...

Canadian Conference on Computer and Robot Vision, CRV

作者： Andrej Janda Pierre Merriaux Pierre Olivier Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Toronto Canada LeddarTech Inc. Québec Canada

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CIDGIKc: Distance-Geometric Inverse Kinematics for Continuum Robots

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IEEE robotics and Automation Letters 2023年第11期8卷 7679-7686页

作者： Zhang, Hanna Jiamei Giamou, Matthew Maric, Filip Kelly, Jonathan Burgner-Kahrs, Jessica University of Toronto Continuum Robotics Laboratory Department of Mathematical and Computational Sciences TorontoONM5S 1A1 Canada McMaster University Autonomous Robotics and Convex Optimization Laboratory Department of Computing and Software HamiltonONL8S 4L8 Canada University of Toronto Space and Terrestrial Autonomous Robotic System Laboratory Institute for Aerospace Studies TorontoONM5S 1A1 Canada

The small size, high dexterity, and intrinsic compliance of continuum robots (CRs) make them well suited for constrained environments. Solving the inverse kinematics (IK), that is finding robot joint configurations that satisfy desired position or pose queries, is a fundamental challenge in motion planning, control, and calibration for any robot structure. For CRs, the need to avoid obstacles in tightly confined workspaces greatly complicates the search for feasible IK solutions. Without an accurate initialization or multiple re-starts, existing algorithms often fail to find a solution. We present CIDGIKc (Convex Iteration for Distance-Geometric Inverse Kinematics for Continuum Robots), an algorithm that solves these nonconvex feasibility problems with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. CIDGIKc is enabled by a novel distance-geometric parameterization of constant curvature segment geometry for CRs with extensible segments. The resulting IK formulation involves only quadratic expressions and can efficiently incorporate a large number of collision avoidance constraints. Our experimental results demonstrate >98% solve success rates within complex, highly cluttered environments which existing algorithms cannot account for. © 2016 IEEE.

关键词： End effectors

Spatiotemporal Calibration of 3D Millimetre-Wavelength Radar-Camera Pairs

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arXiv 2022年

作者： Wise, Emmett Cheng, Qilong Kelly, Jonathan The Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada

autonomous vehicles (AVs) often depend on multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in combination;although radar measurements are sparse in comparison to camera images, radar scans are able to penetrate fog, rain, and snow. Data from both sensors are typically fused prior to use in downstream perception tasks. However, accurate sensor fusion depends upon knowledge of the spatial transform between the sensors and any temporal misalignment that exists in their measurement times. During the life cycle of an AV, these calibration parameters may change, so the ability to perform in-situ spatiotemporal calibration is essential to ensure reliable long-term operation. State-of-the-art 3D radar-camera spatiotemporal calibration algorithms require bespoke calibration targets that are not readily available in the field. In this paper, we describe an algorithm for targetless spatiotemporal calibration that is able to operate without specialized infrastructure. Our approach leverages the ability of the radar unit to measure its own ego-velocity relative to a fixed, external reference frame. We analyze the identifiability of the spatiotemporal calibration problem and determine the motions necessary for calibration. Through a series of simulation studies, we characterize the sensitivity of our algorithm to measurement noise. Finally, we demonstrate accurate calibration for three real-world systems, including a handheld sensor rig and a vehicle-mounted sensor array. Our results show that we are able to match the performance of an existing, target-based method, while calibrating in arbitrary, infrastructure-free environments. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

CIDGIKc: Distance-Geometric Inverse Kinematics for Continuum Robots

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arXiv 2023年

作者： Zhang, Hanna Jiamei Giamou, Matthew Marić, Filip Kelly, Jonathan Burgner-Kahrs, Jessica Continuum Robotics Laboratory Department of Mathematical & Computational Sciences University of Toronto Canada Space & Terrestrial Autonomous Robotic System Laboratory Institute for Aerospace Studies University of Toronto Canada

关键词： Inverse kinematics