Pub Date : 2024-12-30DOI: 10.1109/JISPIN.2023.3348000
{"title":"IEEE Journal of Indoor and Seamless Positioning and Navigation Publication Information","authors":"","doi":"10.1109/JISPIN.2023.3348000","DOIUrl":"https://doi.org/10.1109/JISPIN.2023.3348000","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/JISPIN.2024.3515573
Valérie Renaudin;Francesco Potortì
{"title":"Advancing Resilient and Trustworthy Seamless Positioning and Navigation: Highlights From the Second Volume of J-ISPIN","authors":"Valérie Renaudin;Francesco Potortì","doi":"10.1109/JISPIN.2024.3515573","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3515573","url":null,"abstract":"","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"iii-iii"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/JISPIN.2024.3509803
Afsaneh Saeidanezhad;Wasim Ahmad;Muhammad A. Imran;Olaoluwa R. Popoola
In the rapidly advancing field of wireless localization, achieving accurate indoor tracking is crucial for the next generation of smart factories, automated workflows, and efficient supply chains. The integration of 5G networks within industrial environments offers high connectivity, yet challenges remain in obtaining the fine-grained positioning required for localization applications. This article presents the development and simulation-based evaluation of the sensor-guided non-line-of-sight (NLoS) corrective localization (SGNCL) algorithm within the 5G New Radio network framework. The proposed algorithm utilizes data integration techniques to effectively mitigate NLoS errors, which are prevalent in complex indoor environments with high delay spreads. We describe the algorithm's design, operational principles, and the comprehensive simulation setup used to assess its performance. In comparison to the minimum variance anchor set, which exhibited a mean error of 2.5 m, the SGNCL algorithm achieved a significant improvement, reducing the mean error to 0.86 m. The results also highlight the algorithm's ability to handle varying delay spreads and sensor densities, ensuring robust localization performance across different scenarios. These findings demonstrate the potential of the SGNCL algorithm to enhance 5G-enabled indoor localization services by addressing NLoS challenges through simulation-based insights.
{"title":"Enhancing Indoor Localization Accuracy in Dense IoT-Integrated 5GNR Networks: Introducing SGNCL for Sensor-Guided NLoS Correction Localization","authors":"Afsaneh Saeidanezhad;Wasim Ahmad;Muhammad A. Imran;Olaoluwa R. Popoola","doi":"10.1109/JISPIN.2024.3509803","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3509803","url":null,"abstract":"In the rapidly advancing field of wireless localization, achieving accurate indoor tracking is crucial for the next generation of smart factories, automated workflows, and efficient supply chains. The integration of 5G networks within industrial environments offers high connectivity, yet challenges remain in obtaining the fine-grained positioning required for localization applications. This article presents the development and simulation-based evaluation of the sensor-guided non-line-of-sight (NLoS) corrective localization (SGNCL) algorithm within the 5G New Radio network framework. The proposed algorithm utilizes data integration techniques to effectively mitigate NLoS errors, which are prevalent in complex indoor environments with high delay spreads. We describe the algorithm's design, operational principles, and the comprehensive simulation setup used to assess its performance. In comparison to the minimum variance anchor set, which exhibited a mean error of 2.5 m, the SGNCL algorithm achieved a significant improvement, reducing the mean error to 0.86 m. The results also highlight the algorithm's ability to handle varying delay spreads and sensor densities, ensuring robust localization performance across different scenarios. These findings demonstrate the potential of the SGNCL algorithm to enhance 5G-enabled indoor localization services by addressing NLoS challenges through simulation-based insights.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"333-342"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10804581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1109/JISPIN.2024.3509801
Shunsei Yamagishi;Lei Jing
The algorithms of the Kalman filters have been used in many papers on the Pedestrian Dead Reckoning (PDR) and attitude estimation for the attitude and heading reference system (AHRS). In this article, one type of the nonlinear Kalman filters, the Unscented Kalman filter (UKF) was researched to reduce computational cost, while maintaining accuracy. One of the issues of the attitude estimation algorithms is that computational cost is large, because of many matrix computations. The computational cost should be reduced for the application of the navigation system for general consumers toward developing low-priced navigation system. In this article, the novel UKF, named “Kaisoku Unscented Kalman Filter (KUKF)” is proposed. It was verified that the proposed KUKF reduced the computational cost about 13.426% comparing with the existing UKF, while almost maintaining accuracy.
{"title":"The Unscented Kalman Filter With Reduced Computation Time for Estimating the Attitude of the Attitude and Heading Reference System","authors":"Shunsei Yamagishi;Lei Jing","doi":"10.1109/JISPIN.2024.3509801","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3509801","url":null,"abstract":"The algorithms of the Kalman filters have been used in many papers on the Pedestrian Dead Reckoning (PDR) and attitude estimation for the attitude and heading reference system (AHRS). In this article, one type of the nonlinear Kalman filters, the Unscented Kalman filter (UKF) was researched to reduce computational cost, while maintaining accuracy. One of the issues of the attitude estimation algorithms is that computational cost is large, because of many matrix computations. The computational cost should be reduced for the application of the navigation system for general consumers toward developing low-priced navigation system. In this article, the novel UKF, named “Kaisoku Unscented Kalman Filter (KUKF)” is proposed. It was verified that the proposed KUKF reduced the computational cost about 13.426% comparing with the existing UKF, while almost maintaining accuracy.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"320-332"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For decades, high-accuracy localization has driven the interest of the research community. Recent cases include augmented reality (AR) and virtual reality (VR), indoor robotics, and drone applications, which have led to the emergence of subcentimeter localization requirements. This study introduces a new approach for high-accuracy localization by utilizing isochrons in injection-locked tunable photonic oscillators, which we referred to as Iso chrons in Photonic Oscillators for Pos itioning (IsoPos). The proposed paradigm shift takes advantage of photonic oscillators' radical frequency tunability and isochron structure to offer an innovative path for measuring the time of arrival (ToA). To achieve precise ToA measurements, IsoPos utilizes the phase shift induced by the incoming user signal. This shift is detected by analyzing the phase response of the receiver, i.e., a photonic oscillator, which is exclusively determined by its isochrons' structure. Furthermore, IsoPos uses the injection-locking method as well as the nonlinear properties of injection-locked photonic oscillators to achieve highly accurate phase synchronization between different positioning nodes. This contributes to a seamless 3-D localization devoid of errors caused by miss-synchronization. Our numerical simulations show that IsoPos achieves sub-1 mm accuracy in 3-D localization, surpassing the precision of existing positioning systems by at least one order of magnitude.
{"title":"Isochrons in Injection Locked Photonic Oscillators: A New Frontier for High-Precision Localization","authors":"Alireza Famili;Georgia Himona;Yannis Kominis;Angelos Stavrou;Vassilios Kovanis","doi":"10.1109/JISPIN.2024.3504396","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3504396","url":null,"abstract":"For decades, high-accuracy localization has driven the interest of the research community. Recent cases include augmented reality (AR) and virtual reality (VR), indoor robotics, and drone applications, which have led to the emergence of subcentimeter localization requirements. This study introduces a new approach for high-accuracy localization by utilizing \u0000<italic>isochrons</i>\u0000 in injection-locked tunable photonic oscillators, which we referred to as \u0000<bold>Iso</b>\u0000<italic>chrons in Photonic Oscillators for</i>\u0000 \u0000<bold>Pos</b>\u0000<italic>itioning</i>\u0000 (IsoPos). The proposed paradigm shift takes advantage of photonic oscillators' radical frequency tunability and isochron structure to offer an innovative path for measuring the time of arrival (ToA). To achieve precise ToA measurements, IsoPos utilizes the phase shift induced by the incoming user signal. This shift is detected by analyzing the \u0000<italic>phase response</i>\u0000 of the receiver, i.e., a photonic oscillator, which is exclusively determined by its isochrons' structure. Furthermore, IsoPos uses the injection-locking method as well as the nonlinear properties of injection-locked photonic oscillators to achieve highly accurate phase synchronization between different positioning nodes. This contributes to a seamless 3-D localization devoid of errors caused by miss-synchronization. Our numerical simulations show that IsoPos achieves sub-1 mm accuracy in 3-D localization, surpassing the precision of existing positioning systems by at least one order of magnitude.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"304-319"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10763456","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1109/JISPIN.2024.3501973
Zaid Bin Tariq;Jayson P. Van Marter;Anand G. Dabak;Naofal Al-Dhahir;Murat Torlak
Bluetooth ranging relies on two-way multicarrier phase difference (MCPD) channel frequency response measurements to mitigate time and phase offsets. However, the challenge of doubled multipath components under the two-way MCPD approach, especially with a low number of snapshots, further degrades the performance of the commonly utilized multiple signal classification (MUSIC) algorithm. In this article, we investigate a reduced complexity signal-subspace-based approach for wireless ranging using bluetooth low energy (BLE) in high multipath environments. We propose a novel signal subspace decomposition (SSD) algorithm where we utilize the span of individual signal subspace eigenvectors for range estimation. We formulate the integration of the Fourier transform and randomized low rank approximation into the SSD algorithm to reduce the computational complexity for better utilization in embedded devices. We then make use of the output features from the estimated pseudospectrum of the individual eigenvectors, obtained from the enhanced SSD algorithm, as an input to the long–short-term-memory (LSTM) recurrent neural network to obtain a data-driven SSD-LSTM wireless range estimator for the BLE. We evaluate our proposed approach using our real-world BLE data for single- and multiple-antenna scenarios. Our results show an improved performance of our proposed approach by more than 37%, while still enjoying the lowest computational complexity than existing MUSIC and support vector regression approaches for BLE ranging.
{"title":"A Data-Driven Signal Subspace Approach for Indoor Bluetooth Ranging","authors":"Zaid Bin Tariq;Jayson P. Van Marter;Anand G. Dabak;Naofal Al-Dhahir;Murat Torlak","doi":"10.1109/JISPIN.2024.3501973","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3501973","url":null,"abstract":"Bluetooth ranging relies on two-way multicarrier phase difference (MCPD) channel frequency response measurements to mitigate time and phase offsets. However, the challenge of doubled multipath components under the two-way MCPD approach, especially with a low number of snapshots, further degrades the performance of the commonly utilized multiple signal classification (MUSIC) algorithm. In this article, we investigate a reduced complexity signal-subspace-based approach for wireless ranging using bluetooth low energy (BLE) in high multipath environments. We propose a novel signal subspace decomposition (SSD) algorithm where we utilize the span of individual signal subspace eigenvectors for range estimation. We formulate the integration of the Fourier transform and randomized low rank approximation into the SSD algorithm to reduce the computational complexity for better utilization in embedded devices. We then make use of the output features from the estimated pseudospectrum of the individual eigenvectors, obtained from the enhanced SSD algorithm, as an input to the long–short-term-memory (LSTM) recurrent neural network to obtain a data-driven SSD-LSTM wireless range estimator for the BLE. We evaluate our proposed approach using our real-world BLE data for single- and multiple-antenna scenarios. Our results show an improved performance of our proposed approach by more than 37%, while still enjoying the lowest computational complexity than existing MUSIC and support vector regression approaches for BLE ranging.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"292-303"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-07DOI: 10.1109/JISPIN.2024.3476011
Georg K.J. Fischer;Niklas Thiedecke;Thomas Schaechtle;Andrea Gabbrielli;Fabian Höflinger;Alexander Stolz;Stefan J. Rupitsch
Angle-of-arrival (AoA) estimation technology, with its potential advantages, emerges as an intriguing choice for indoor localization. Notably, it holds the promise of reducing installation costs. In contrast to time-of-flight (ToF)/time-difference-of-arrival (TDoA) based systems, AoA-based approaches require a reduced number of nodes for effective localization. This characteristic establishes a tradeoff between installation costs and the complexity of hardware and software. Moreover, the appeal of acoustic localization is further heightened by its capacity to provide cost-effective hardware solutions while maintaining a high degree of accuracy. Consequently, acoustic AoA estimation technology stands out as a feasible and compelling option in the field of indoor localization. Sparse arrays additionally have the ability to estimate the direction-of-arrival (DoA) of more sources than available sensors by placing sensors in a specific geometry. In this contribution, we introduce a measurement platform designed to evaluate various sparse array geometries experimentally. The acoustic microphone array comprises 64 microphones arranged in an 8×8 grid, following an uniform rectangular array (URA) configuration, with a grid spacing of 8.255 mm. This configuration achieves a spatial Nyquist frequency of approximately 20.8 kHz in the acoustic domain at room temperature. Notably, the array exhibits a mean spherical error of 1.26 $^{circ }$ when excluding higher elevation angles. The platform allows for masking sensors to simulate sparse array configurations. We assess four array geometries through simulations and experimental data, identifying the open-box and nested array geometries as robust candidates. In addition, we demonstrate the array's capability to concurrently estimate the directions of three emitting sources using experimental data, employing waveforms consisting of orthogonal codes.
{"title":"Evaluation of Sparse Acoustic Array Geometries for the Application in Indoor Localization","authors":"Georg K.J. Fischer;Niklas Thiedecke;Thomas Schaechtle;Andrea Gabbrielli;Fabian Höflinger;Alexander Stolz;Stefan J. Rupitsch","doi":"10.1109/JISPIN.2024.3476011","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3476011","url":null,"abstract":"Angle-of-arrival (AoA) estimation technology, with its potential advantages, emerges as an intriguing choice for indoor localization. Notably, it holds the promise of reducing installation costs. In contrast to time-of-flight (ToF)/time-difference-of-arrival (TDoA) based systems, AoA-based approaches require a reduced number of nodes for effective localization. This characteristic establishes a tradeoff between installation costs and the complexity of hardware and software. Moreover, the appeal of acoustic localization is further heightened by its capacity to provide cost-effective hardware solutions while maintaining a high degree of accuracy. Consequently, acoustic AoA estimation technology stands out as a feasible and compelling option in the field of indoor localization. Sparse arrays additionally have the ability to estimate the direction-of-arrival (DoA) of more sources than available sensors by placing sensors in a specific geometry. In this contribution, we introduce a measurement platform designed to evaluate various sparse array geometries experimentally. The acoustic microphone array comprises 64 microphones arranged in an 8×8 grid, following an uniform rectangular array (URA) configuration, with a grid spacing of 8.255 mm. This configuration achieves a spatial Nyquist frequency of approximately 20.8 kHz in the acoustic domain at room temperature. Notably, the array exhibits a mean spherical error of 1.26\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000 when excluding higher elevation angles. The platform allows for masking sensors to simulate sparse array configurations. We assess four array geometries through simulations and experimental data, identifying the open-box and nested array geometries as robust candidates. In addition, we demonstrate the array's capability to concurrently estimate the directions of three emitting sources using experimental data, employing waveforms consisting of orthogonal codes.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"263-274"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Because of the drone's penetration into our society, the demand for their indoor positioning has increased. However, its standard technology has not been established yet. This article describes an indoor 3-D tracking method for drones, using the drone's built-in camera to capture light reflected from the floor. Using a captured image and video data captured during the drone's flight, the proposed method can estimate the drone's position and trajectory. A drone's built-in camera is usually unable to capture light directly from ceiling light sources because of its limited field of view and gimbal angles. To address this problem, the proposed method captures the light indirectly, as the reflections from the floor of ceiling light-emitting diodes (LEDs), in the video stream acquired by its rolling-shutter camera. The 3-D position is estimated by calculating the received signal strength of each individual LED for a single video frame during the flight and fitting this data to a model generated by simulation images. In an indoor environment without external lights, we captured the reflected light from floor surfaces using the drone's camera under gimbal control and analyzed the captured images offline. Experimental results gave an absolute error of 0.34 m at the 90th percentile for 3-D positioning when hovering and using a single-frame image. For a linear flight path, the error was 0.31 m. The computation time for 3-D position estimation was 1.12 s. We also discussed limitations related to real-time and real-world applications, together with approaches to addressing these limitations.
随着无人机在社会中的普及，人们对其室内定位的需求也越来越高。然而，其标准技术尚未建立。本文介绍了一种利用无人机内置摄像头捕捉地面反射光的室内三维跟踪方法。利用捕捉到的图像和无人机飞行过程中捕捉到的视频数据，所提出的方法可以估算出无人机的位置和轨迹。由于无人机的视野和万向节角度有限，其内置摄像头通常无法直接捕捉来自天花板光源的光线。为了解决这个问题，所提出的方法是在滚动快门相机获取的视频流中间接捕捉天花板发光二极管（LED）从地面反射的光线。通过计算飞行过程中单个视频帧中每个 LED 的接收信号强度，并将该数据与模拟图像生成的模型进行拟合，从而估算出三维位置。在没有外部灯光的室内环境中，我们使用无人机的摄像头在云台控制下捕捉地板表面的反射光，并对捕捉到的图像进行离线分析。实验结果表明，在悬停和使用单帧图像时，3-D 定位的绝对误差在第 90 百分位数为 0.34 米。我们还讨论了与实时和实际应用相关的局限性，以及解决这些局限性的方法。
{"title":"Indoor Drone 3-D Tracking Using Reflected Light From Floor Surfaces","authors":"Yusei Onishi;Hiroki Watanabe;Masanari Nakamura;Hiromichi Hashizume;Masanori Sugimoto","doi":"10.1109/JISPIN.2024.3453775","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3453775","url":null,"abstract":"Because of the drone's penetration into our society, the demand for their indoor positioning has increased. However, its standard technology has not been established yet. This article describes an indoor 3-D tracking method for drones, using the drone's built-in camera to capture light reflected from the floor. Using a captured image and video data captured during the drone's flight, the proposed method can estimate the drone's position and trajectory. A drone's built-in camera is usually unable to capture light directly from ceiling light sources because of its limited field of view and gimbal angles. To address this problem, the proposed method captures the light indirectly, as the reflections from the floor of ceiling light-emitting diodes (LEDs), in the video stream acquired by its rolling-shutter camera. The 3-D position is estimated by calculating the received signal strength of each individual LED for a single video frame during the flight and fitting this data to a model generated by simulation images. In an indoor environment without external lights, we captured the reflected light from floor surfaces using the drone's camera under gimbal control and analyzed the captured images offline. Experimental results gave an absolute error of 0.34 m at the 90th percentile for 3-D positioning when hovering and using a single-frame image. For a linear flight path, the error was 0.31 m. The computation time for 3-D position estimation was 1.12 s. We also discussed limitations related to real-time and real-world applications, together with approaches to addressing these limitations.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"251-262"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1109/JISPIN.2024.3433671
Park Kunbum;Tsuchiya Takeshi
This study proposes a method that employs a renderer as a tool for environmental recognition. In the proposed system, features are extracted from sensors and cameras; the renderer represents scenes in a 3-D space to suit the purpose of the applications, and the applications resample the scenes to achieve their purpose after manipulating the renderer. As an example, this study presents implementation mechanisms of environmental recognition—odometry, map-building, navigation, and localization of automotive indoor robots. This method has a higher computational cost than typical feature-based methods; however, the algorithms are considerably intuitive. Although commercial rendering engines cannot be used as they are, a lightweight rendering engine dedicated to recognition can operate in embedded systems to enable real-time recognition. In addition, this study presents an experiment that corresponds to the simulation of moving robots indoors. In conclusion, this study proposes a change from the perspective of adopting a renderer–a well-established software technology that has been thoroughly investigated and can manipulate space–as an essential tool in the recognition framework.
{"title":"Employing an Embedded Renderer as Recognition Tool for Odometry, Map-Building, Navigation, and Localization on Active Sensing Robotics","authors":"Park Kunbum;Tsuchiya Takeshi","doi":"10.1109/JISPIN.2024.3433671","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3433671","url":null,"abstract":"This study proposes a method that employs a renderer as a tool for environmental recognition. In the proposed system, features are extracted from sensors and cameras; the renderer represents scenes in a 3-D space to suit the purpose of the applications, and the applications resample the scenes to achieve their purpose after manipulating the renderer. As an example, this study presents implementation mechanisms of environmental recognition—odometry, map-building, navigation, and localization of automotive indoor robots. This method has a higher computational cost than typical feature-based methods; however, the algorithms are considerably intuitive. Although commercial rendering engines cannot be used as they are, a lightweight rendering engine dedicated to recognition can operate in embedded systems to enable real-time recognition. In addition, this study presents an experiment that corresponds to the simulation of moving robots indoors. In conclusion, this study proposes a change from the perspective of adopting a renderer–a well-established software technology that has been thoroughly investigated and can manipulate space–as an essential tool in the recognition framework.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"275-291"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations.
{"title":"LuViRA Dataset Validation and Discussion: Comparing Vision, Radio, and Audio Sensors for Indoor Localization","authors":"Ilayda Yaman;Guoda Tian;Erik Tegler;Jens Gulin;Nikhil Challa;Fredrik Tufvesson;Ove Edfors;Kalle Åström;Steffen Malkowsky;Liang Liu","doi":"10.1109/JISPIN.2024.3429110","DOIUrl":"https://doi.org/10.1109/JISPIN.2024.3429110","url":null,"abstract":"In this article, we present a unique comparative analysis, and evaluation of vision-, radio-, and audio-based localization algorithms. We create the first baseline for the aforementioned sensors using the recently published Lund University Vision, Radio, and Audio dataset, where all the sensors are synchronized and measured in the same environment. Some of the challenges of using each specific sensor for indoor localization tasks are highlighted. Each sensor is paired with a current state-of-the-art localization algorithm and evaluated for different aspects: localization accuracy, reliability and sensitivity to environment changes, calibration requirements, and potential system complexity. Specifically, the evaluation covers the Oriented FAST and Rotated BRIEF simultaneous localization and mapping (SLAM) algorithm for vision-based localization with an RGB-D camera, a machine learning algorithm for radio-based localization with massive multiple-input multiple-output (MIMO) technology, and the StructureFromSound2 algorithm for audio-based localization with distributed microphones. The results can serve as a guideline and basis for further development of robust and high-precision multisensory localization systems, e.g., through sensor fusion, and context- and environment-aware adaptations.","PeriodicalId":100621,"journal":{"name":"IEEE Journal of Indoor and Seamless Positioning and Navigation","volume":"2 ","pages":"240-250"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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