The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform
Euan Freeman - One of the best experts on this subject based on the ideXlab platform.
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VRST - A Quest for Co-Located Mixed Reality: Aligning and Assessing SLAM Tracking for Same-Space Multi-User Experiences
26th ACM Symposium on Virtual Reality Software and Technology, 2020Co-Authors: Mark Mcgill, Jan Gugenheimer, Euan FreemanAbstract:Current solutions for creating co-located Mixed Reality (MR) experiences typically rely on platform-specific synchronisation of spatial anchors or Simultaneous Localisation and Mapping (SLAM) data across clients, often coupled to cloud services. This introduces significant costs (in development and deployment), constraints (with interoperability across platforms often limited), and privacy concerns. For practitioners, support is needed for creating platform-agnostic co-located MR experiences. This paper explores the utility of aligned SLAM solutions by 1) surveying approaches toward aligning disparate Device Coordinate spaces, formalizing their theoretical accuracy and limitations; 2) providing skeleton implementations for audience-based, small-scale and large-scale co-location using said alignment approaches; and 3) detailing how we can assess the accuracy and safety of 6DoF/SLAM tracking solutions for any arbitrary Device and dynamic environment without the need for an expensive ground truth optical tracking, by using trilateration and a $30 laser distance meter. Through this, we hope to further democratise the creation of cross-platform co-located MR experiences.
Mark Mcgill - One of the best experts on this subject based on the ideXlab platform.
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VRST - A Quest for Co-Located Mixed Reality: Aligning and Assessing SLAM Tracking for Same-Space Multi-User Experiences
26th ACM Symposium on Virtual Reality Software and Technology, 2020Co-Authors: Mark Mcgill, Jan Gugenheimer, Euan FreemanAbstract:Current solutions for creating co-located Mixed Reality (MR) experiences typically rely on platform-specific synchronisation of spatial anchors or Simultaneous Localisation and Mapping (SLAM) data across clients, often coupled to cloud services. This introduces significant costs (in development and deployment), constraints (with interoperability across platforms often limited), and privacy concerns. For practitioners, support is needed for creating platform-agnostic co-located MR experiences. This paper explores the utility of aligned SLAM solutions by 1) surveying approaches toward aligning disparate Device Coordinate spaces, formalizing their theoretical accuracy and limitations; 2) providing skeleton implementations for audience-based, small-scale and large-scale co-location using said alignment approaches; and 3) detailing how we can assess the accuracy and safety of 6DoF/SLAM tracking solutions for any arbitrary Device and dynamic environment without the need for an expensive ground truth optical tracking, by using trilateration and a $30 laser distance meter. Through this, we hope to further democratise the creation of cross-platform co-located MR experiences.
Jan Gugenheimer - One of the best experts on this subject based on the ideXlab platform.
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VRST - A Quest for Co-Located Mixed Reality: Aligning and Assessing SLAM Tracking for Same-Space Multi-User Experiences
26th ACM Symposium on Virtual Reality Software and Technology, 2020Co-Authors: Mark Mcgill, Jan Gugenheimer, Euan FreemanAbstract:Current solutions for creating co-located Mixed Reality (MR) experiences typically rely on platform-specific synchronisation of spatial anchors or Simultaneous Localisation and Mapping (SLAM) data across clients, often coupled to cloud services. This introduces significant costs (in development and deployment), constraints (with interoperability across platforms often limited), and privacy concerns. For practitioners, support is needed for creating platform-agnostic co-located MR experiences. This paper explores the utility of aligned SLAM solutions by 1) surveying approaches toward aligning disparate Device Coordinate spaces, formalizing their theoretical accuracy and limitations; 2) providing skeleton implementations for audience-based, small-scale and large-scale co-location using said alignment approaches; and 3) detailing how we can assess the accuracy and safety of 6DoF/SLAM tracking solutions for any arbitrary Device and dynamic environment without the need for an expensive ground truth optical tracking, by using trilateration and a $30 laser distance meter. Through this, we hope to further democratise the creation of cross-platform co-located MR experiences.
Yan Huang - One of the best experts on this subject based on the ideXlab platform.
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Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM
IEEE Access, 2019Co-Authors: Qu Wang, Haiyong Luo, Aidong Men, Fang Zhao, Yan HuangAbstract:Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on Device attitude. We leverage the STNs to align the Device Coordinate system and the navigation Coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52°, mean heading error of 6.07° and heading error of 9.18° at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications.
Qu Wang - One of the best experts on this subject based on the ideXlab platform.
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Pedestrian Heading Estimation Based on Spatial Transformer Networks and Hierarchical LSTM
IEEE Access, 2019Co-Authors: Qu Wang, Haiyong Luo, Aidong Men, Fang Zhao, Yan HuangAbstract:Accurate heading estimation is the foundation of numerous applications, including augmented reality, pedestrian dead reckoning, and human-computer interactions. While magnetometer is a key source of heading information, the poor accuracy of consumer-grade hardware coupled with the pervasive magnetic disturbances makes accurate heading estimation a challenging issue. Heading error is one of the main error sources of pedestrian dead reckoning. To reduce the heading error and enhance robustness, we proposed a novel heading estimation method based on Spatial Transformer Networks (STNs) and Long Short-Term Memory (LSTM), termed DeepHeading, which uses sensors embedded in a smartphone without any historical training data or dedicated infrastructure. We automatically annotate heading data based on map matching, and augment heading data based on Device attitude. We leverage the STNs to align the Device Coordinate system and the navigation Coordinate system, allow an unconstrained use of smartphones. Based on the characteristics of pedestrian heading continuity, we designed a hierarchical LSTM-basedSeq2Seq model to estimate the walking heading of the pedestrian. We conducted well-designed experiments to evaluate the performance of deepheading and compared it with the state-of-the-art heading estimation algorithms. The experimental results on real-world demonstrated that deepheading outperformed the compared heading estimation algorithms and achieved promising estimation accuracy with a median heading error of 4.52°, mean heading error of 6.07° and heading error of 9.18° at the confidence of 80% when a pedestrian is walking in indoor environments with magnetic field disturbances. The proposed method is high-efficiency and easy to integrate with various mobile applications.