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Advancements and Performance Analysis of Feature Detection and Matching Methods in Visual Odometry

Journal of Institute of Control, robotics and Systems 2025年第3期31卷 231-236页

作者： Choi, Ji Hun Kang, Chang Ho Kim, Sun Young Department of Artificial Intelligence Sejong University Korea Republic of Department of AI Robotics Sejong University Korea Republic of School of Mechanical Engineering Kunsan National University Korea Republic of

This study presents a comprehensive performance analysis of feature detectors and descriptors in visual odometry (VO) based on four key metrics: absolute trajectory error (ATE), relative pose error (RPE), cumulative drift, and frames per second (FPS). Five algorithm-based methods (SIFT, ROOTSIFT, ORB, KAZE, and AKAZE) and five ai-based methods (SuperPoint, DISK, ALIKED, D2NET, and Xfeat) are evaluated using two matching strategies: Brute-Force and LIGHTGLUE. Performance assessment is conducted on the KITTI dataset, demonstrating that KAZE achieves the best results in ATE and cumulative drift, while ALIKED outperforms in RPE and FPS. These findings offer valuable insights for selecting the optimal feature detection and description techniques in real-world VO applications. © ICROS 2025.

关键词： Deep learning

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Nonlinear Dynamic Inversion-based Motion Planning of a Floating Satellite Platform

Nonlinear Dynamic Inversion-based Motion Planning of a Float...

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aiAA Science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Banerjee, Avijit Tevetzidis, Ilias Satpute, Sumeet Gajanan Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satellite’s realistic movement in space, thus evolving as an efficient demonstration tool as a hardware-in-loop satellite motion simulator. A brief design description of the emulation platform unified with various sensor and actuator units is presented, and an associated nonlinear mathematical model representing its coupled dynamic motion is described. The proposed control design architecture manipulates the associated nonlinear dynamics, which appear due to the coupling of translational and rotational movement of the robotic platform, thereby establishing an efficient trajectory tracking capability for various operating regions without the additional requirement of gain scheduling. In order to realize the control requirement through a set of ON-OFF thruster-based actuation units, which closely resembles the reaction control system of realistic satellite design, an optimal thrust allocation logic is proposed that accounts for various physical limitations of thrusters such as thrusters as its magnitude constraint and minimum on time. The efficacy of the proposed modelling and control scheme is experimentally validated in different realistic motion planning scenarios, such as point-to-point manoeuvres with terminal objectives and a superior path-following capability. The experimental studies are supported with related results. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Motion planning

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Quadtree-based Free-space Cell-decomposition for Pathplanning with RRT* Implementation

Quadtree-based Free-space Cell-decomposition for Pathplannin...

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aiAA Science and Technology Forum and Exposition, aiAA SciTech Forum 2025

作者： Otte, Noah Satpute, Sumeet Gajanan Banerjee, Avijit Nikolakopoulos, George Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper introduces a novel, computationally efficient, random-search based path-planning algorithm specifically designed to enhance the dexterous accessibility of autonomous lunar rovers operating on highly cluttered obstacle-dense planetary surfaces. The proposed pathplanning framework employs a Quadtree-based cell decomposition to enhance the performance of sampling-based path-planning algorithms, specifically those in the category of Rapidlyexploring Random Trees (RRT) and its optimized variant, RRT*. In the process of generating the path through an occupancy grid map, the obstacle-free cells are utilized as discrete sampling points for the RRT* algorithm. A unique non-uniform sampling approach is employed to favour larger cells with a higher degree of safety, thereby enabling seamless navigation through the obstacle-dense map. Additionally, the non-uniform sampling approach enhances the overall computational efficiency of the framework, making it suitable for fast, lightweight onboard implementation. The efficacy of the proposed planning framework is demonstrated through numerous simulation scenarios. © 2025, American Institute of Aeronautics and Astronautics Inc, aiAA. All rights reserved.

关键词： Decision trees

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InstantPose: Zero-Shot Instance-Level 6D Pose Estimation from a Single View

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IEEE robotics and Automation Letters 2025年第6期10卷 6023-6030页

作者： Felice, Francesco Di Remus, Alberto Gasperini, Stefano Busam, Benjamin Ott, Lionel Thalhammer, Stefan Tombari, Federico Avizzano, Carlo Alberto Scuola Superiore sant'anna Department of Excellence in Robotics & AI Mechanical Intelligence Institute Pisa Italy Leonardo S.p.A. Robotics Laboratory Innovation Hub & Intellectual Property Genoa Italy Technical University of Munich TUM School of Computation Information and Technology Germany ETH Zurich Department of Mechanical and Process Engineering Autonomous Systems Lab Switzerland UAS Technikum Vienna Industrial Engineering Department Austria Google Zurich Switzerland

Object pose estimation using visual data is crucial for robotic interaction with the environment. Many existing instance-level methods are restricted by their requirements for 3D CAD models or multiple object views, which limits their flexibility and generalizability. Overcoming this limitation is critical to enhance the adaptability of pose estimation systems. In this work, a novel pipeline that leverages recent advances in reconstruction techniques is presented to address these challenges. To this end, Large Reconstruction Models (LRM) represent an advanced neural architecture capable of generating 3D object models from a limited set of views. Nevertheless, the resulting 3D models often lack relevant geometric and texture details due to insufficient input information. This research presents InstantPose, an innovative zero-shot instance-level pose estimation method that, building upon LRM, can determine the pose of unseen objects using as little as a single unposed RGB reference and RGB-D query images. Extensive experiments demonstrate that InstantPose achieves remarkable performance in object pose estimation on the YCB-V dataset, compared to methods conceived to rely on a geometrically perfect object's model. Furthermore, the 6D pose provided through the presented approach facilitates successful object grasping, highlighting its practical utility in robotic manipulation tasks. © 2016 IEEE.

关键词： 3D reconstruction

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The Effect of User Learning for Online EEG Decoding of Upper-Limb Movement Intention

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 633-641页

作者： Ceradini, Matteo Tortora, Stefano Micera, Silvestro Tonin, Luca University of Padova Department of Information Engineering Padova Italy University of Padova Padua Neuroscience Center Padova Italy Scuola Superiore Sant'Anna BioRobotics Institute Department of Excellence in Robotics and AI Pisa Italy Translational NeuroEngineering Laboratory Neuro-X Institute Lausanne Switzerland Laboratory Milano Italy

Electroencephalography (EEG) based braincomputer interfaces (BCIs) offer a promising way for individuals with motor impairments to control prosthetic or rehabilitation devices. Accurately decoding movement intention (MI) is crucial for translating subjects' motor execution plans into action. Common challenges in EEG-based BCIs include performance discrepancies, often requiring frequent recalibration of decoding algorithms. The objective of this study was enhancing BCI decoding performance of upper-limb MI identification by exploiting both machine and subjects learning and maintaining stable decoding algorithms. Significant performance improvements were observed across most subjects from the first to the last session of the experiment. Some subjects also demonstrated stable performance without requiring any model recalibration between sessions. All subjects achieved high efficacy in online decoding of movement intention, as reflected in improvement of the F1 score from 0.5810.26 in the first session, to 0.84±0.13 in the final session. We emphasize the critical importance of allowing users sufficient time to improve their performance in BCIs for upper-limb MI decoding. Unlike existing studies, we specifically evaluate the effect of stable decoding strategies in online and longitudinal BCI sessions, which are key to achieving more reliable and effective BCIs. © 2018 IEEE.

关键词： Brain computer interface

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Safety Inspections and Gas Monitoring in Hazardous Mining Areas Shortly After Blasting Using Autonomous UAVs

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Journal of Field robotics 2025年第0期

作者： Nordström, Samuel Stathoulopoulos, Nikolaos Dahlquist, Niklas Lindqvist, Björn Tevetzidis, Ilias Kanellakis, Christoforos Nikolakopoulos, George Robotics & AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This article presents the first ever fully autonomous UAV (Unmanned Aerial Vehicle) mission to perform gas measurements after a real blast in an underground mine. The demonstration mission was deployed around 40 min after the blast took place, and as such realistic gas levels were measured. We also present multiple field robotics experiments in different mines detailing the development process. The presented novel autonomy stack, denoted as the Routine Inspection Autonomy (RIA) framework, combines a risk-aware 3D path planning (Formula presented.), with 3D LiDAR-based global relocalization on a known map, and it is integrated on a custom hardware and a sensing stack with an onboard gas sensing device. In the presented framework, the autonomous UAV can be deployed in incredibly harsh conditions (dust, significant deformations of the map) shortly after blasting to perform inspections of lingering gases that present a significant safety risk to workers. We also present a change detection framework that can extract and visualize the areas that were changed in the blasting procedure, a critical parameter for planning the extraction of materials, and for updating existing mine maps. As will be demonstrated, the RIA stack can enable robust autonomy in harsh conditions, and provides reliable and safe navigation behavior for autonomous Routine Inspection missions. © 2025 The Author(s). Journal of Field robotics published by Wiley Periodicals LLC.

关键词： Unmanned aerial vehicles (UAV)

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Neuroethology of natural actions in freely moving monkeys

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Science (New York, N.Y.) 2025年第6730期387卷 214-220页

作者： Lanzarini, Francesca Maranesi, Monica Rondoni, Elena Hilary Albertini, Davide Ferretti, Elena Lanzilotto, Marco Micera, Silvestro Mazzoni, Alberto Bonini, Luca Department of Medicine and Surgery University of Parma Parma Italy Biorobotics Institute Department of Excellence in AI and Robotics Scuola Superiore Sant'Anna Pisa Italy Interdisciplinary Health Center and Department of Excellence in AI and Robotics Scuola Superiore Sant'Anna Pisa Italy Modular Implantable Neuroprostheses Laboratory Università Vita-Salute San Raffaele & Scuola Superiore Sant'Anna Milan Italy Bertarelli Foundation Chair in Translational Neuroengineering School of Engineering Ecole Polytechnique Federale de Lausanne Genève Switzerland

The current understanding of primate natural action organization derives from laboratory experiments in restrained contexts (RCs) under the assumption that this knowledge generalizes to freely moving contexts (FMCs). In this work, we developed a neurobehavioral platform to enable wireless recording of the same premotor neurons in both RCs and FMCs. Neurons often encoded the same hand and mouth actions differently in RCs and FMCs. Furthermore, in FMCs, we identified cells that selectively encoded actions untestable during RCs and others that displayed mixed selectivity for multiple actions, which is compatible with an organization based on cortical motor synergies at different levels of complexity. Cross-context decoding demonstrated that neural activity in FMCs is richer and more generalizable than in RCs, which suggests that neuroethological approaches are better suited to unveil the neural bases of behavior.

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Multi-Agent Path Finding Using Conflict-Based Search and Structural-Semantic Topometric Maps

arXiv

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arXiv 2025年

作者： Fredriksson, Scott Bai, Yifan Saradagi, Akshit Nikolakopoulos, George Robotics and AI group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

As industries increasingly adopt large robotic fleets, there is a pressing need for computationally efficient, practical, and optimal conflict-free path planning for multiple robots. Conflict-Based Search (CBS) is a popular method for multi-agent path finding (MAPF) due to its completeness and optimality;however, it is often impractical for real-world applications, as it is computationally intensive to solve and relies on assumptions about agents and operating environments that are difficult to realize. This article proposes a solution to overcome computational challenges and practicality issues of CBS by utilizing structural-semantic topometric maps. Instead of running CBS over large grid-based maps, the proposed solution runs CBS over a sparse topometric map containing structural-semantic cells representing intersections, pathways, and dead ends. This approach significantly accelerates the MAPF process and reduces the number of conflict resolutions handled by CBS while operating in continuous time. In the proposed method, robots are assigned time ranges to move between topometric regions, departing from the traditional CBS assumption that a robot can move to any connected cell in a single time step. The approach is validated through real-world multi-robot path-finding experiments and benchmarking simulations. The results demonstrate that the proposed MAPF method can be applied to real-world non-holonomic robots and yields significant improvement in computational efficiency compared to traditional CBS methods while improving conflict detection and resolution in cases of corridor symmetries. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

arXiv

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arXiv 2025年

作者： Suarez, Fausto Mauricio Lagos Saradagi, Akshit Sumathy, Vidya Kotpalliwar, Shruti Nikolakopoulos, George Robotics and AI group in the Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This article introduces a curriculum learning approach to develop a reinforcement learning-based robust stabilizing controller for a Quadrotor that meets predefined performance criteria. The learning objective is to achieve desired positions from random initial conditions while adhering to both transient and steady-state performance specifications. This objective is challenging for conventional one-stage end-to-end reinforcement learning, due to the strong coupling between position and orientation dynamics, the complexity in designing and tuning the reward function, and poor sample efficiency, which necessitates substantial computational resources and leads to extended convergence times. To address these challenges, this work decomposes the learning objective into a three-stage curriculum that incrementally increases task complexity. The curriculum begins with learning to achieve stable hovering from a fixed initial condition, followed by progressively introducing randomization in initial positions, orientations and velocities. A novel additive reward function is proposed, to incorporate transient and steady-state performance specifications. The results demonstrate that the Proximal Policy Optimization (PPO)based curriculum learning approach, coupled with the proposed reward structure, achieves superior performance compared to a single-stage PPO-trained policy with the same reward function, while significantly reducing computational resource requirements and convergence time. The curriculum-trained policy’s performance and robustness are thoroughly validated under random initial conditions and in the presence of disturbances. © 2025, CC BY.

关键词： Reinforcement learning

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WQuatNet: Wide range quaternion-based head pose estimation

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Journal of King Saud University - Computer and Information Sciences 2025年第3期37卷 1-14页

作者： Algabri, Redhwan Shin, Hyunsoo Abdu, Ahmed Bae, Ji-Hun Lee, Sungon Dept. of Computer Science & Engineering College of AI Convergence Sejong University Seoul 05006 South Korea Research Institute of Engineering and Technology Hanyang University Ansan 15588 South Korea School of Software Northwestern Polytechnical University Xi’an 710072 China Department of Robotics Hanyang University Ansan 15588 South Korea

Head pose estimation (HPE) is a critical task for numerous applications ranging from human-computer interaction, healthcare, and robotics, to surveillance. Most existing methods employ Euler angles as a representation, which often face challenges such as a gimbal lock, especially in full-range rotation scenarios or rotation matrices that require nine parameters. This study introduces WQuatNet, a novel deep learning-based model that leverages the quaternion representation, which uses only four parameters, to avoid this challenge. WQuatNet was designed based on a landmark-free HPE method to predict head poses across the full-range angles of 360∘ from images. Landmark-free methods bypass the need for explicit detection of facial landmarks;instead, they leverage the entire image to estimate the head orientation. The model incorporates a RepVGG-D2se backbone for robust feature extraction and introduces two loss functions tailored for quaternion predictions. Our experimental results on multiple HPE datasets covering both narrow- and full-range angles demonstrate that WQuatNet outperforms the state-of-the-art (SOTA) approaches in terms of accuracy. The performance of the proposed HPE was evaluated using the CMU, AGORA, BIWI, AFLW2000, and 300W-LP datasets. We also perform ablation studies and error analyses to validate the significance of each component of the model. © The Author(s) 2025.

关键词： Deep neural network Full range of rotation Head pose estimation Quaternion

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