
Navigation Fusion Engineer – GPS-Denied
- Fort Wayne, IN
- Permanent
- Full-time
- Design and implement advanced sensor fusion algorithms using Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and Nonlinear Observers.
- Develop and refine tightly coupled and loosely coupled navigation solutions integrating IMU, odometry, vision (VIO/SVO), LiDAR, magnetometers, and barometers.
- Evaluate and deploy factor graph-based SLAM and probabilistic inference frameworks (e.g., GTSAM, iSAM2) for persistent localization in GPS-denied environments.
- Contribute to algorithm development for multi-sensor fusion frameworks, including Deep Learning-Augmented Inertial Navigation, event-based sensors, and semantic fusion.
- Conduct performance evaluations under degraded or denied GNSS conditions, including urban canyons, subterranean, and electronic warfare environments.
- Work cross-functionally with autonomy, guidance, perception, and embedded software teams.
- Develop and support simulation frameworks (e.g., Gazebo, AirSim, ROS2) for testing navigation systems in virtual environments.
- 10+ years of experience in navigation algorithm development with proven track record on deployed systems.
- Expert in Kalman Filter variants (EKF, UKF, IEKF, Particle Filters) and modern Bayesian estimation methods.
- Strong experience with GNSS-denied navigation techniques including:
- Visual-Inertial Odometry (VIO/SVO)
- LiDAR-Inertial Odometry (LIO)
- DVL integration (for maritime)
- Zero-velocity updates (ZUPT) for foot-mounted or stationary UGVs
- Cooperative Localization / SWaP-C-constrained fusion for munitions
- Proficiency with C++ and Python in real-time or embedded systems.
- Familiarity with ROS/ROS2, GTSAM, NavFusion engines like NavVis, u-blox, and VectorNav SDKs.
- Experience in hardware-in-the-loop (HIL) testing and integration with real-time operating systems (RTOS).
- Experience with:
- Factor Graph optimization, graph-SLAM, and incremental smoothing (iSAM2)
- Deep learning-enhanced navigation (e.g., DeepVO, LSTM-IMU fusion)
- Event-based camera fusion
- Cold-start estimation and initialization robustness
- Knowledge of STAN, BayesOpt, or GP-based estimation for adaptive sensor tuning.
- Publications or patents in navigation, estimation theory, or multi-sensor fusion.
- An exciting career path with opportunities for continuous learning and development.
- Research oriented work, alongside award winning teams developing practical solutions for our nation’s security
- Flexible schedules with every other Friday off work, if desired (9/80 schedule)
- Competitive benefits, including 401k matching, flex time off, paid parental leave, healthcare benefits, health & wellness programs, employee resource and social groups, and more
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