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robot localization aided by quantum algorithms

robotICS AND AUTONOMOUS SYSTEMS 2025年 192卷

作者： Antero, Unai Sierra, Basilio Onativia, Jona Ruiz, Alejandra Osaba, Eneko TECNALIA Basque Res & Technol Alliance BRTA Mikeletegi Pasealekua 7 Donostia San Sebastian 20009 Gipuzkoa Spain Univ Basque Country UPV EHU Robot & Autonomous Syst Grp RSAIT Manuel Lardizabal 1 San Sebastian 20018 Gipuzkoa Spain

localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.

关键词： Quantum computing robot localization Mobile robotics Grover's algorithm Navigation and mapping Autonomous systems

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A Reliable robot localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy

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IEEE SENSORS JOURNAL 2024年第22期24卷 37846-37858页

作者： Tang, Wei Huang, Anmin Liu, Enbo Wu, Jiale Zhang, Renyuan Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China

Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.

关键词： Three-dimensional displays Global navigation satellite system (GNSS) LiDAR outdoor mobile robot robot localization sensor fusion Three-dimensional displays Global navigation satellite system (GNSS) LiDAR outdoor mobile robot robot localization sensor fusion

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Acoustic Echo Sensing for robot localization in Buried Pipe Networks

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IEEE SENSORS JOURNAL 2024年第16期24卷 26506-26521页

作者： Worley, Rob Yu, Yicheng Horoshenkov, Kirill V. Anderson, Sean R. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN England Univ Sheffield Dept Mech Engn Sheffield S10 2TN England

robot localization in buried pipes presents many challenges, including the unavailability of sensing methods, such as a global positioning system, and the limited perspective of sensors, such as vision. This article addresses these challenges by using acoustic sensing, where sound emitted by robots propagates long distances and around corners in the pipe environment and is used to estimate the distance to reflective features. The reverberant environment produces many echoes that contain useful information for localization, but it also produces many second-order echoes that have reflected from two features consecutively, which creates a challenge for localization. Therefore, a novel approach for using first- and second-order echoes in pose-graph optimization for localization is developed here. Experiments demonstrated that the acoustic echo localization approach gave a relative estimate error of 0.24% (with 0.23% standard deviation). We compared to the results of a Kalman filter localization algorithm (3.4% error) and a pose-graph optimization alternative designed for only first-order echoes (0.80% error) and also compared to results from sensing approaches in the literature including vision sensing (2.17%-2.27% error), acoustic sensing with a separate transmitter (3.5% error), and radio beacon sensing (0.047% error). This suggests that acoustic echo sensing surpasses these alternative methods, except those that require external hardware, and gives a tenfold improvement on other approaches without external hardware. In summary, we have developed a new method for echo localization in pipes, incorporating second-order echoes into pose-graph optimization, and demonstrated its effectiveness with respect to alternative approaches.

关键词： robot sensing systems Sensors robots Acoustics Position measurement Location awareness Acoustic measurements Acoustic sensing pipe network inspection processing of acoustic sensor data robot localization simultaneous localization and mapping (SLAM)

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UWB-Based robot localization Using Distributed Adaptive EFIR Filtering

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 30704-30713页

作者： Xu, Yuan Zang, Xin Shmaliy, Yuriy S. Yu, Jingwen Zhuang, Yuan Sun, Mingxu Univ Jinan Sch Elect Engn Jinan 250022 Peoples R China Shandong Beiming Med Technol Co Ltd Jinan 250000 Peoples R China Univ Guanajuato Dept Elect Engn Salamanca 36885 Mexico Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

Ultrawideband (UWB)-based localization is widely used in environments inaccessible to global navigation satellite system signals. To improve the precision of UWB-based localization, a robust distributed adaptive extended unbiased finite impulse response (EFIR) filtering algorithm is developed. The algorithm is designed to reduce round-off errors and adaptively adjust noise covariances using the expectation-maximization (EM) approach. Based on extensive experimental testing, the EFIR algorithm is shown to outperform the distributed extended Kalman filter-based algorithm and distributed EFIR filter-based algorithm under harsh conditions.

关键词： robots robot kinematics Noise Location awareness robot localization Internet of Things Accuracy Distributed extended unbiased finite impulse response (EFIR) filter expectation-maximization (EM) robot localization ultrawideband (UWB)

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MetaSonic: Advancing robot localization With Directional Embedded Acoustic Signals

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IEEE robotICS AND AUTOMATION LETTERS 2025年第2期10卷 1704-1711页

作者： Wang, Junling An, Zhenlin Guo, Yi Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manage Shanghai 200240 Peoples R China Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

Indoor positioning in environments where GPS cannot be used is a fundamental technology for robot navigation and human-robot interaction. However, existing vision-based localization systems cannot work in low-visibility environments, and existing wireless or acoustic localization systems require specific transceivers, making them expensive and power-intensive - particularly challenging for microrobots. This letter proposes a new metasurface-assisted ultrasound positioning system. The key idea is to use a low-cost passive acoustic metasurface to transfer any speaker into a directional sound source, with the acoustic spectrum varying based on direction. This allows any microrobot with a simple, low-cost microphone to capture such modified sound to identify the direction of the sound source. We develop a lightweight convolutional neural network-based localization algorithm that can be efficiently deployed on low-power microcontrollers. We evaluate our system in a large complex office. It can achieve a direction estimation accuracy of 7.26 degrees, improving by 42.2% compared to systems without the metasurface and matching the performance of a 4-microphone array, with a localization accuracy of 0.35 m.

关键词： Location awareness Acoustics robots Accuracy Metasurfaces Synchronization Microphone arrays Estimation robot localization Encoding localization robot audition deep learning angle estimation acoustic metasurface

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Global UWB System: A High-Accuracy Mobile robot localization System With Tightly Coupled Integration

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16618-16626页

作者： Bach, Sy-Hung Khoi, Phan-Bui Yi, Soo-Yeong Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea Hanoi Univ Sci & Technol Sch Mech Engn Hanoi 10000 Vietnam

Autonomous mobile robots (AMRs) play a crucial role in smart factories and Internet of Things (IoT) applications. This article presents a high-accuracy localization system called the global UWB system (GUS) for the AMR based on ultra-wideband (UWB) distance measurements. By using two UWB modules (tags) on AMR, it is possible to estimate both position and heading angle simultaneously, which can solve the problem of error accumulation over time in internal sensors. A new tightly coupled algorithm called baseline constraint extended Kalman filter (BC-EKF) is proposed. The baseline is the actual distance between the two tags that is used as a constraint for the measurement process. The relationship between the baseline and the heading angle error is also discussed to ensure reasonable deployment of the tags. The proposed localization method is persistent as value of the geometric dilution of precision (GDOP) increases. This helps to secure the performance of the localization method even in tight workspaces where the number of fixed UWBs (anchors) is limited. The simulation and experiment results demonstrate the localization performance of the proposed method.

关键词： Extended Kalman filter (EKF) global UWB system (GUS) robot localization two-way ranging (TWR) ultra-wideband (UWB)

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Extended Kalman/UFIR Filters for UWB-Based Indoor robot localization Under Time-Varying Colored Measurement Noise

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IEEE INTERNET OF THINGS JOURNAL 2023年第17期10卷 15632-15641页

作者： Xu, Yuan Shmaliy, Yuriy S. Bi, Shuhui Chen, Xiyuan Zhuang, Yuan Jinan Univ Sch Elect Engn Jinan 250022 Peoples R China Univ Guanajuato Dept Elect Engn Salamanca 36885 Mexico Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

In indoor robot localization by using ultra-wideband (UWB), the extended Kalman filter (EKF)-based algorithms suffer from the colored measurement noise (CMN) that degrades the localization accuracy and causes the divergence. To overcome this issue, we develop a hybrid colored EKF and colored extended unbiased finite impulse response (EFIR) filter (cEKF/EFIR filter) employing measurement differences. We also develop this algorithm using a filter bank on merged averaging horizons to be adaptive to time-varying CMN and call it the adaptive EKF/EFIR (aEKF/EFIR) filter. Experimental testing is provided in UWB-based indoor mobile robot localization environments. It is shown that the end-to-end colored EKF/EFIR and aEKF/EFIR filtering algorithms have better performances than the EKF, EFIR filter, and their modifications for CMN.

关键词： Location awareness robots robot kinematics Noise measurement Internet of Things Global Positioning System robot localization Colored measurement noise extended Kalman filter (EKF) extended UFIR (EFIR) filter ultra-wideband (UWB) localization

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A visual questioning answering approach to enhance robot localization in indoor environments

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FRONTIERS IN NEUROrobotICS 2023年 17卷 1290584页

作者： Pena-Narvaez, Juan Diego Martin, Francisco Guerrero, Jose Miguel Perez-Rodriguez, Rodrigo Rey Juan Carlos Univ Int Doctoral Sch Signal Theory Commun Telemat Syst & Computat Dept Intelligent Robot Lab Fuenlabrada Spain Rey Juan Carlos Univ Intelligent Robot Lab Signal Theory Commun Telemat Syst & Computat Dept Fuenlabrada Spain

Navigating robots with precision in complex environments remains a significant challenge. In this article, we present an innovative approach to enhance robot localization in dynamic and intricate spaces like homes and offices. We leverage Visual Question Answering (VQA) techniques to integrate semantic insights into traditional mapping methods, formulating a novel position hypothesis generation to assist localization methods, while also addressing challenges related to mapping accuracy and localization reliability. Our methodology combines a probabilistic approach with the latest advances in Monte Carlo localization methods and Visual Language models. The integration of our hypothesis generation mechanism results in more robust robot localization compared to existing approaches. Experimental validation demonstrates the effectiveness of our approach, surpassing state-of-the-art multi-hypothesis algorithms in both position estimation and particle quality. This highlights the potential for accurate self-localization, even in symmetric environments with large corridor spaces. Furthermore, our approach exhibits a high recovery rate from deliberate position alterations, showcasing its robustness. By merging visual sensing, semantic mapping, and advanced localization techniques, we open new horizons for robot navigation. Our work bridges the gap between visual perception, semantic understanding, and traditional mapping, enabling robots to interact with their environment through questions and enrich their map with valuable insights. The code for this project is available on GitHub https://***/juandpenan/topology_nav_ros2.

关键词： visual question answering robot localization robot navigation semantic map robot mapping

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Multi-faceted deep learning framework for dynamics modeling and robot localization learning

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 5541-5550页

作者： Shan, Yuxiang Lu, Hailiang Lou, Weidong China Tobacco Zhejiang Ind Co Ltd Hangzhou Zhejiang Peoples R China

Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc.

关键词： robot localization dynamics modeling learning-based robot decision

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High-Precision Mobile robot localization Using the Integration of RAR and AKF

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2023年第5期E106D卷 1001-1009页

作者： Wang, Chen Tan, Hong Nanjing Tech Univ Coll Comp Sci & Technol Nanjing 211816 Peoples R China

The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Re-lax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The po-sitioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti -gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental val-idation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the ex-tended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.

关键词： robot localization curve fitting time synchronization Kalman filter adaptive Kalman filter

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