Demo Data from EDBT 2026 / reviewers in the wild / expert
Vikram Bhat
dblp:305/4335
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% | |
| Computer networks
1 paper |
Wireless sensing and localization · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › augmented reality
collaborative augmented reality |
0.6 | 1 | 2022 | Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging · IPSN 2022 |
Wireless sensing and localization › ranging
ultra-wideband ranging |
0.2 | 1 | 2022 | Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer Ranging · IPSN 2022 |
Methods — techniques the papers use, named apart from their topics
visual-inertial odometry · 1.1particle filtering · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Cappella: Establishing Multi-User Augmented Reality Sessions Using Inertial Estimates and Peer-to-Peer RangingabstractCurrent collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter. John Miller 0002, Elahe Soltanaghai, Raewyn Duvall, Jeff Chen, Vikram Bhat, Nuno Pereira 0001, Anthony Rowe 0001 |
IPSN | 5 |