International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation最新文献
Pub Date : 2024-10-01Epub Date: 2024-12-12DOI: 10.1109/ipin62893.2024.10786145
Chia Hsuan Tsai, Roberto Manduchi
Navigating unfamiliar environments can be challenging for visually impaired individuals due to difficulties in recognizing distant landmarks or visual cues. This work focuses on a particular form of wayfinding, specifically backtracking a previously taken path, which can be useful for blind pedestrians. We propose a hands-free indoor navigation solution using a smartphone without relying on pre-existing maps or external infrastructure. Our hybrid matching method integrates machine learning to enhance positioning accuracy, addressing real-life challenges such as odometry errors or deviations from the correct path. Testing with datasets from visually impaired individuals demonstrates the potential of our approach in providing reliable backtracking assistance.
{"title":"Robust Indoor Pedestrian Backtracking Using Magnetic Signatures and Inertial Data.","authors":"Chia Hsuan Tsai, Roberto Manduchi","doi":"10.1109/ipin62893.2024.10786145","DOIUrl":"10.1109/ipin62893.2024.10786145","url":null,"abstract":"<p><p>Navigating unfamiliar environments can be challenging for visually impaired individuals due to difficulties in recognizing distant landmarks or visual cues. This work focuses on a particular form of wayfinding, specifically backtracking a previously taken path, which can be useful for blind pedestrians. We propose a hands-free indoor navigation solution using a smartphone without relying on pre-existing maps or external infrastructure. Our hybrid matching method integrates machine learning to enhance positioning accuracy, addressing real-life challenges such as odometry errors or deviations from the correct path. Testing with datasets from visually impaired individuals demonstrates the potential of our approach in providing reliable backtracking assistance.</p>","PeriodicalId":510887,"journal":{"name":"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049443","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 : 2024-10-01Epub Date: 2024-12-12DOI: 10.1109/ipin62893.2024.10786167
Yunqian Cheng, Roberto Manduchi
In this paper, we present PALMS, an innovative indoor global localization and relocalization system for mobile smartphones that utilizes publicly available floor plans. Unlike most vision-based methods that require constant visual input, our system adopts a dynamic form of localization that considers a single instantaneous observation and odometry data. The core contribution of this work is the introduction of a particle filter initialization method that leverages the Certainly Empty Space (CES) constraint along with principal orientation matching. This approach creates a spatial probability distribution of the device's location, significantly improving localization accuracy and reducing particle filter convergence time. Our experimental evaluations demonstrate that PALMS outperforms traditional methods with uniformly initialized particle filters, providing a more efficient and accessible approach to indoor wayfinding. By eliminating the need for prior environmental fingerprinting, PALMS provides a scalable and practical approach to indoor navigation.
{"title":"PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones.","authors":"Yunqian Cheng, Roberto Manduchi","doi":"10.1109/ipin62893.2024.10786167","DOIUrl":"10.1109/ipin62893.2024.10786167","url":null,"abstract":"<p><p>In this paper, we present PALMS, an innovative indoor global localization and relocalization system for mobile smartphones that utilizes publicly available floor plans. Unlike most vision-based methods that require constant visual input, our system adopts a dynamic form of localization that considers a single instantaneous observation and odometry data. The core contribution of this work is the introduction of a particle filter initialization method that leverages the Certainly Empty Space (CES) constraint along with principal orientation matching. This approach creates a spatial probability distribution of the device's location, significantly improving localization accuracy and reducing particle filter convergence time. Our experimental evaluations demonstrate that PALMS outperforms traditional methods with uniformly initialized particle filters, providing a more efficient and accessible approach to indoor wayfinding. By eliminating the need for prior environmental fingerprinting, PALMS provides a scalable and practical approach to indoor navigation.</p>","PeriodicalId":510887,"journal":{"name":"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049442","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 : 2023-09-01Epub Date: 2023-12-06DOI: 10.1109/ipin57070.2023.10332483
Fatemeh Elyasi, Roberto Manduchi
Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step.
{"title":"Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning.","authors":"Fatemeh Elyasi, Roberto Manduchi","doi":"10.1109/ipin57070.2023.10332483","DOIUrl":"10.1109/ipin57070.2023.10332483","url":null,"abstract":"<p><p>Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step.</p>","PeriodicalId":510887,"journal":{"name":"International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050046","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}
International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号