activity
20152026
most citedOn-Manifold Preintegration for Real-Time Visual-Inertial Odometry

1.5k citations · 2k across the 42 of their papers we have counts for

collaborators

47 papers

cs.RO2026

FAST-Sync: Fast Group Synchronization for any Matrix Lie Group

Shane Holmes, Yiran Luo, Firat Taxpulat +2

Group synchronization (GS) is the problem of estimating a set of unknown elements in a group , given noisy measurements of a subs…

cs.RO2026

Equivariant Filter Design for Acoustic and Depth Aided Inertial Navigation Systems

Arihant Lunawat, Pieter van Goor, Frank Dellaert +1

Autonomous Underwater Vehicles (AUVs) navigating without GPS typically fuse inertial measurements with acoustic Doppler Velocity Log (DVL) velocities and pressure-derived depth. Po…

cs.CV2026

Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field

Shangjie Xue, Jesse Dill, Dhruv Ahuja +3

We present Gaussian Splatting Anisotropic Visibility Field (GAVIS), a novel framework for uncertainty quantification and active mapping in 3DGS. Our key insight is that regions uns…

cs.RO2026

Variable Elimination in Hybrid Factor Graphs for Discrete-Continuous Inference & Estimation

Varun Agrawal, Frank Dellaert

Many problems in robotics involve both continuous and discrete components, and modeling them together for estimation tasks has been a long standing and difficult problem. Hybrid Fa…

cs.CV2024

SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas

John Lambert, Yuguang Li, Ivaylo Boyadzhiev +6

We propose a new system for automatic 2D floorplan reconstruction that is enabled by SALVe, our novel pairwise learned alignment verifier. The inputs to our system are sparsely loc…

cs.CV2024★ 1 cited

Neural Visibility Field for Uncertainty-Driven Active Mapping

Shangjie Xue, Jesse Dill, Pranay Mathur +3

This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that re…