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Innopolis University

Academic institutioneurope · ru
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Research library154linked papers
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Selected work

Representative Papers

Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment

Sep 01, 20182018 International Conference on Intelligent Systems (IS)

This study addresses the performance evaluation of simultaneous localization and mapping (SLAM) in structured indoor environments. We systematically benchmark ten mainstream open-source SLAM algorithms—spanning 2D LiDAR, monocular, and stereo camera modalities—using a unified, real-world multimodal dataset collected in an office setting, with synchronized 2D LiDAR scans, monocular RGB frames, and ZED stereo images. A standardized evaluation framework is established, incorporating quantitative metrics including absolute trajectory error (ATE), map completeness, and real-time execution capability. To our knowledge, this is the first work to conduct a cross-sensor, cross-algorithm comparative analysis on identical real-world data, revealing systematic trade-offs among accuracy, robustness, and operational applicability. Results indicate that Cartographer (LiDAR-based), ORB-SLAM2 (monocular), and RTAB-Map (stereo) achieve the best overall performance. This work provides a reproducible benchmark and empirical foundation for sensor selection and algorithmic improvement in multi-modal SLAM.

150 citations3 influentialRead paper

MSA-technique for stiffness modeling of manipulators with complex and hybrid structures

Nov 19, 2025IFAC Symposium on Robot Control

Traditional stiffness modeling methods struggle to simultaneously achieve accuracy, computational efficiency, and topological adaptability for complex hybrid-configured robotic manipulators featuring closed-loop kinematics, flexible links, rigid/elastic joints, and coupled preload–external load conditions. To address this, this paper proposes a Modular Stiffness Analysis (MSA) framework that integrates matrix structural analysis, screw theory, and finite-element discretization. It is the first work to systematically introduce a modular strategy into stiffness modeling, enabling flexible composition and rapid analytical derivation for diverse configurations—including rigid–flexible coupling and parallel topologies. The resulting global stiffness matrix achieves over 30% higher computational efficiency compared to conventional approaches, with modeling errors bounded by ≤5%. Comprehensive validation across multiple representative hybrid-architecture manipulators demonstrates both high accuracy and strong generalizability.

12 citationsRead paper

Vision-Language Models Unlock Task-Centric Latent Actions

Jan 30, 2026

This work addresses the vulnerability of existing latent action models to task-irrelevant distractors, which often leads to the erroneous encoding of noise as action signals. To mitigate this, the authors propose a novel approach that leverages the commonsense reasoning capabilities of vision-language models (VLMs) to generate task-aware representations distinguishing controllable dynamics from noise. Specifically, task-oriented natural language prompts—such as “ignore distractors”—are used to guide VLMs in producing supervision signals that, in an unsupervised setting, steer latent action models toward learning task-centric action representations. Evaluated on the Distracting MetaWorld benchmark, the method improves downstream task success rates by up to sixfold, significantly enhances action semantic consistency, and effectively suppresses interference. The study also reveals notable differences in prompt sensitivity and performance across various VLMs in the context of action representation learning.

1 citationsRead paper
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