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Tree Instance Segmentation and Traits Estimation for Forestry Environments Exploiting LiDAR Data Collected by Mobile Robots

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Tree Instance Segmentation and Traits Estimation for Forestr...

IEEE International Conference on robotics and Automation (ICRA)

作者： Meher V. R. Malladi Tiziano Guadagnino Luca Lobefaro Matias Mattamala Holger Griess Janine Schweier Nived Chebrolu Maurice Fallon Jens Behley Cyrill Stachniss Center for Robotics University of Bonn Germany University of Oxford UK Swiss Federal Institute for Forest Snow and Landscape Research Switzerland Department of Engineering Science University of Oxford UK Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Forests play a crucial role in our ecosystems, functioning as carbon sinks, climate stabilizers, biodiversity hubs, and sources of wood. By the very nature of their scale, monitoring and maintaining forests is a challenging task. robotics in forestry can have the potential for substantial automation toward efficient and sustainable foresting practices. In this paper, we address the problem of automatically producing a forest inventory by exploiting LiDAR data collected by a mobile platform. To construct an inventory, we first extract tree instances from point clouds. Then, we process each instance to extract forestry inventory information. Our approach provides the per-tree geometric trait of "diameter at breast height" together with the individual tree locations in a plot. We validate our results against manual measurements collected by foresters during field trials. Our experiments show strong segmentation and tree trait estimation performance, underlining the potential for automating forestry services. Results furthermore show a superior performance compared to the popular baseline methods used in this domain.

关键词： Instance segmentation Point cloud compression Forests Laser radar Pipelines Semantics Estimation

Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping

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arXiv 2025年

作者： Wang, Dong Haag, Hannes Herraez, Daniel Casado May, Stefan Stachniss, Cyrill Nüchter, Andreas Informatics XVII – Robotics Julius-Maximilians-Universität Würzburg Germany Faculty of Electrical Engineering Precision Engineering Information Technology Nuremberg Institute of Technology Georg Simon Ohm Germany CARIAD SE University of Bonn Germany Center for Robotics University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Simultaneous localization and mapping (SLAM) is a critical capability for autonomous systems. Traditional SLAM approaches, which often rely on visual or LiDAR sensors, face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://***/Wayne-DWA/Doppler-SLAM. © 2025, CC BY-NC-ND.

关键词： Mapping

Kernelized Offline Contextual Dueling Bandits

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arXiv 2023年

作者： Mehta, Viraj Neopane, Ojash Das, Vikramjeet Lin, Sen Schneider, Jeff Neiswanger, Willie Robotics Institute Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Computer Science Stanford University United States

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and introduce the offline contextual dueling bandit setting. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound. We also give empirical confirmation that this method outperforms a similar strategy that uses uniformly sampled contexts. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

BATS: Best Action Trajectory Stitching

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arXiv 2022年

作者： Char, Ian Mehta, Viraj Villaflor, Adam Dolan, John M. Schneider, Jeff Department of Machine Learning Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

The problem of offline reinforcement learning focuses on learning a good policy from a log of environment interactions. Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data. In this work, we explore an alternative approach by planning on the fixed dataset directly. Specifically, we introduce an algorithm which forms a tabular Markov Decision Process (MDP) over the logged data by adding new transitions to the dataset. We do this by using learned dynamics models to plan short trajectories between states. Since exact value iteration can be performed on this constructed MDP, it becomes easy to identify which trajectories are advantageous to add to the MDP. Crucially, since most transitions in this MDP come from the logged data, trajectories from the MDP can be rolled out for long periods with confidence. We prove that this property allows one to make upper and lower bounds on the value function up to appropriate distance metrics. Finally, we demonstrate empirically how algorithms that uniformly constrain the learned policy to the entire dataset can result in unwanted behavior, and we show an example in which simply behavior cloning the optimal policy of the MDP created by our algorithm avoids this problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

Sim2real Transfer learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly

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arXiv 2023年

作者： Wu, Chengzhi Bi, Xuelei Pfrommer, Julius Cebulla, Alexander Mangold, Simon Beyerer, Jürgen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Germany Fraunhofer Center for Machine Learning Germany wbk Institute of Production Science Karlsruhe Institute of Technology Germany

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Morphological symmetries in robotics

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arXiv 2024年

作者： Apraez, Daniel Ordoñez Turrisi, Giulio Kostic, Vladimir Martin, Mario Agudo, Antonio Moreno-Noguer, Francesc Pontil, Massimiliano Semini, Claudio Mastalli, Carlos Italy Computational Statistics and Machine Learning IIT Italy Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain Robot Motor Intelligence – Heriot-Watt University United Kingdom IHMC Robotics Florida Institute for Human & Machine Cognition United States

We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot’s morphology, frequently observed in animal biology and robotics, which stem from the replication of kinematic structures and the symmetrical distribution of mass. We illustrate how these symmetries extend to the robot’s state space and both proprioceptive and exteroceptive sensor measurements, resulting in the equivariance of the robot’s equations of motion and optimal control policies. Thus, we recognize morphological symmetries as a relevant and previously unexplored physics-informed geometric prior, with significant implications for both data-driven and analytical methods used in modeling, control, estimation and design in robotics. For data-driven methods, we demonstrate that morphological symmetries can enhance the sample efficiency and generalization of machine learning models through data augmentation, or by applying equivariant/invariant constraints on the model’s architecture. In the context of analytical methods, we employ abstract harmonic analysis to decompose the robot’s dynamics into a superposition of lower-dimensional, independent dynamics. We substantiate our claims with both synthetic and real-world experiments conducted on bipedal and quadrupedal robots. Lastly, we introduce the repository MORPHOSYMM to facilitate the practical use of the theory and applications outlined in this *** Codes 68T40 © 2024, CC BY.

关键词： Robots

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

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CITR: A Coordinate-Invariant Task Representation for Robotic...

IEEE International Conference on robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

Radar-Only Odometry and Mapping for Autonomous Vehicles

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Radar-Only Odometry and Mapping for Autonomous Vehicles

IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Casado Herraez Matthias Zeller Le Chang Ignacio Vizzo Michael Heidingsfeld Cyrill Stachniss CARIAD SE and With the University of Bonn Germany CARIAD SE and With the University of Stuttgart Germany Dexory UK University of Bonn is With CARIAD SE Germany Center for Robotics University of Bonn and With the Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP approach that leverages the velocity information provided by 3D radar sensors. The second one is advantageous for 2D radars with a low number of samples, and particularly useful for scenarios where the sensor is being blocked by large dynamic obstacles. It exploits a constant velocity filter and the measured Doppler velocities to estimate the vehicle’s ego-motion. We enrich this with a filtering step to improve the accuracy of the points in the resulting map. We put our work to the test using the View of Delft and NuScenes datasets, which involve 3D and 2D radar sensors. Our findings illustrate state-of-the-art performance of our odometry techniques in terms of accuracy when compared to existing alternatives. Moreover, we demonstrate that our map filtering methodology achieves higher similarity rates than the raw unfiltered map when benchmarked against a corresponding LiDAR map.

关键词： Three-dimensional displays Accuracy Laser radar Radar measurements Navigation Doppler radar Sensors

Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces

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arXiv 2023年

作者： de Ocáriz Borde, Haitz Sáez Arroyo, Álvaro López, Ismael Morales Posner, Ingmar Dong, Xiaowen Oxford Robotics Institute University of Oxford United Kingdom Oxford-Man Institute University of Oxford United Kingdom Mathematical Institute University of Oxford United Kingdom Machine Learning Research Group University of Oxford United Kingdom

Recent studies propose enhancing machine learning models by aligning the geometric characteristics of the latent space with the underlying data structure. Instead of relying solely on Euclidean space, researchers have suggested using hyperbolic and spherical spaces with constant curvature, or their combinations (known as product manifolds), to improve model performance. However, there exists no principled technique to determine the best latent product manifold signature, which refers to the choice and dimensionality of manifold components. To address this, we introduce a novel notion of distance between candidate latent geometries using the Gromov-Hausdorff distance from metric geometry. We propose using a graph search space that uses the estimated Gromov-Hausdorff distances to search for the optimal latent geometry. In this work we focus on providing a description of an algorithm to compute the Gromov-Hausdorff distance between model spaces and its computational implementation. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry