Loosely Coupled UWB/Stereo Camera Integration for Mobile Robots Indoor Navigation

Glukhov, O. V.; Akinfiev, Ivan A.; Razorvin, Andrey D.; Chugunov, Alexander; Gutarev, Dmitriy A.; Serov, Sergey A.

doi:10.1109/reepe57272.2023.10086807

Cited by 2 publications

(1 citation statement)

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“…They developed a segmented structure for independent navigation using deep reinforcement learning via ROS2. In the meantime, Glukhov et al [12] explored the integration of UWB and stereo cameras for indoor mobile robot navigation, while Khnissi et al [13] implemented an optimized SLAM system based on ROS for mobile robots. Wang et al [14] employed a fuzzy neural network for navigation control, and Liu et al [15] introduced graph relational reinforcement learning for large-scale crowded environments.…”

Section: Introductionmentioning

confidence: 99%

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Khan,

Kunwar,

Khan

et al. 2023

IEEE Access

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The primary objective of this study is to investigate the effects of mobile robot navigation using a fuzzy logic framework based on Z-numbers implemented within the Robot Operating System (ROS) Noetic. The methodology addresses uncertainty and imprecise information in robot navigation using extensive simulations performed using the TurtleBot3 robot in the ROS framework. Our unique approach enables the autonomous navigation of mobile robots in unknown environments, utilizing fuzzy rules with multiple inputs and outputs. The navigation strategy relies on the laser scan sensor, the Adaptive Monte Carlo Localization (AMCL) algorithm, and particle filter mapping, enabling real-time localization and mapping capabilities. Path planning incorporates local and global planners, while obstacle avoidance generates collision-free paths by dynamically detecting and circumventing obstacles in the robot's proximity. We employ Simultaneous Localization and Mapping (SLAM) techniques to estimate the robot's position and create a map of the environment. Our integration of these methods presents a promising solution for autonomous mobile robot navigation in real-world applications, thereby advancing the capabilities of robot systems in complex environments. Our results demonstrate the suitability and effectiveness of using a Z-number-based system in the navigation scenarios of mobile robots.

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Section: Introductionmentioning

confidence: 99%

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Khan,

Kunwar,

Khan

et al. 2023

IEEE Access

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Adaptive Visual-Inertial Odometry Using SuperGlue and Dynamic EKF with Information-Based Confidence Estimation

ASIL,

Nasibov

2024

Preprint

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This study presents an innovative Visual-Inertial Odometry (VIO) approach for Unmanned Aerial Vehicles (UAVs). The proposed system integrates the modern deep learning-based SuperGlue algorithm with an information based adaptive Extended Kalman Filter (EKF). The system establishes a dynamic confidence estimation mechanism using image entropy, intensity variation, and motion blur metrics, thereby providing robust pose estimation even in challenging environmental conditions. Thanks to the superior performance of advanced transformer-based feature matching methods like SuperGlue, the proposed loosely-coupled sensor fusion technique approaches the accuracy of traditional tightly-coupled approaches, potentially offering an alternative to these methods. Comprehensive experiments conducted on the EuRoC MAV dataset demonstrate that the proposed method provides significant improvements compared to conventional approaches. Particularly in challenging scenarios, an improvement of approximately 50% was observed in the estimation of quaternions and Euler angles. This study reveals that loosely-coupled sensor fusion, when combined with advanced feature matching techniques and adaptive filtering strategies, can offer a robust alternative to tightly-coupled approaches. The results highlight the potential applications of the proposed method in fields such as robotic navigation, autonomous vehicles, and augmented reality. Additionally, the Python code associated with our study has been shared as open-source on GitHub for use in other academic studies at "\href{https://github.com/ufukasia/Adaptive-VIO-Odometry}{https://github.com/ufukasia/Adaptive-VIO-Odometry}".

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Loosely Coupled UWB/Stereo Camera Integration for Mobile Robots Indoor Navigation

Cited by 2 publications

References 22 publications

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

Adaptive Visual-Inertial Odometry Using SuperGlue and Dynamic EKF with Information-Based Confidence Estimation

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