Patrick Geneva

About

Patrick Geneva is a robotics researcher whose work sits at the intersection of state estimation, sensor fusion, and autonomous navigation. His research primarily focuses on visual-inertial navigation systems (VINS), multi-sensor fusion, and robust localization for mobile robots and autonomous platforms. Geneva has made significant contributions to the development of tightly-coupled estimators that combine cameras, inertial measurement units (IMUs), and LiDAR sensors to achieve accurate six-degree-of-freedom pose estimation in complex, real-world environments. Among his most recognized contributions is LIC-Fusion 2.0, which introduced a sliding-window filter approach for LiDAR-inertial-camera odometry, accumulating over 160 citations and establishing itself as a landmark work in multi-modal sensor fusion. His MIMC-VINS framework, with nearly 90 citations, demonstrated the versatility of multi-IMU, multi-camera systems for resilient 3D motion tracking. Geneva has also advanced the theory of IMU intrinsic calibration observability and pioneered linear-complexity EKF approaches for scalable visual-inertial localization with loop closures. His more recent work on NeRF-VINS integrates neural radiance fields into real-time navigation pipelines, showcasing his continued push toward next-generation mapping and localization solutions. Across his portfolio, Geneva's research has collectively garnered nearly 400 citations, reflecting his growing influence in the robotics and autonomous systems community.

Research Focus

Key Achievements

Top Papers

1. 1
   LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking

   162 citations · 2020
2. 2
   MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System

   89 citations · 2021
3. 3
   A Linear-Complexity EKF for Visual-Inertial Navigation with Loop Closures

   39 citations · 2019
4. 4
   Online IMU Intrinsic Calibration: Is It Necessary?

   38 citations · 2020
5. 5
   NeRF-VINS: A Real-time Neural Radiance Field Map-based Visual-Inertial Navigation System

   19 citations · 2024
6. 6
   Schmidt-EKF-based Visual-Inertial Moving Object Tracking

   19 citations · 2020
7. 7
   Distributed Visual-Inertial Cooperative Localization

   16 citations · 2021
8. 8
   Versatile 3D Multi-Sensor Fusion for Lightweight 2D Localization

   11 citations · 2020
9. 9
   LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking

   5 citations · 2020
10. 10
    Distributed Visual-Inertial Cooperative Localization

    2 citations · 2021

Key Collaborators

Contact & Links