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Kalman Filtering Inertial Navigation Jobs in Chicago, IL

Ascend Engineering

Autonomy Engineer (UAV)

Chicago, IL · On-site

$100K - $160K/yr

Tune and analyze estimation and control performance (EKF/Kalman filtering, PID loops) across ... Experience with visual-inertial odometry (VIO), map matching, or GPS-denied navigation * Experience ...

Ascend Engineering

Autonomy Engineer (UAV)

Chicago, IL · On-site

Tune and analyze estimation and control performance (EKF/Kalman filtering, PID loops) across ... Experience with visual-inertial odometry (VIO), map matching, or GPS-denied navigation * Experience ...

All JobsKalman Filtering Inertial Navigation JobsKalman Filtering Inertial Navigation Jobs in Chicago, IL

Kalman Filtering Inertial Navigation information

What is Kalman filtering in inertial navigation?

Kalman Filtering in inertial navigation refers to the use of a mathematical algorithm—the Kalman filter—to estimate the position, velocity, and orientation of a moving object by processing data from inertial sensors such as accelerometers and gyroscopes. The Kalman filter improves the accuracy of navigation by optimally combining sensor measurements and correcting for errors and noise. This technique is essential in applications like aerospace, robotics, and autonomous vehicles, where precise movement tracking is required. By continuously updating estimates as new sensor data arrives, Kalman filtering helps provide reliable and real-time navigation solutions.

What are some common challenges faced when implementing Kalman filtering for inertial navigation systems?

One common challenge in this role is managing sensor noise and drift, which can significantly affect the accuracy of inertial navigation solutions. You'll need to carefully tune filter parameters and sometimes integrate additional sensor data (like GPS or magnetometers) to improve robustness. Collaboration with hardware teams is typical, as understanding sensor characteristics is crucial for optimal filter performance. Additionally, real-time processing constraints often require you to optimize algorithms for efficiency without sacrificing accuracy.

What are the key skills and qualifications needed to thrive as a Kalman filtering inertial navigation engineer, and why are they important?

To thrive as a Kalman Filtering Inertial Navigation Engineer, you need a solid background in control systems, signal processing, and estimation theory, generally supported by a degree in electrical engineering, aerospace engineering, or a related field. Proficiency with MATLAB, Python, sensor fusion algorithms, and experience implementing Kalman filters in embedded systems are typically required. Strong analytical thinking, attention to detail, and effective communication skills help professionals collaborate across multidisciplinary teams and solve complex problems. These skills and qualifications are vital to ensure accurate navigation solutions and robust system performance in real-world applications.

What is the difference between Kalman Filtering Inertial Navigation vs INS Algorithm Developer?

AspectKalman Filtering Inertial NavigationINS Algorithm Developer
CredentialsEngineering degrees, knowledge of Kalman filters, navigation systemsEngineering degrees, expertise in navigation algorithms, sensor fusion
Work EnvironmentResearch labs, aerospace, defense, autonomous vehicle developmentSoftware development, simulation, embedded systems in similar industries
Industry UsageDesign and implementation of navigation systems using Kalman filtersDeveloping algorithms for inertial navigation systems, improving accuracy

Kalman Filtering Inertial Navigation focuses on applying Kalman filters to process sensor data for navigation accuracy, while INS Algorithm Developers design and optimize algorithms for inertial navigation systems. Both roles require similar technical skills but differ in their primary focus: one on filtering techniques, the other on algorithm development.

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Kalman Filtering Inertial Navigation jobs near you

Autonomy Engineer (UAV)

Ascend Engineering

Chicago, IL • On-site

$100K - $160K/yr

Full-time

Re-posted 4 days ago

Job description

We are seeking a Robotics & Autonomy Engineer to develop, integrate, and maintain autonomy capabilities on UAV platforms built around the PX4 and ArduPilot stacks. This is a full-time role on our engineering team, working across a range of client projects - so the day-to-day emphasizes hands-on integration, debugging, and real-world flight validation more than ground-up design ownership. You'll work across the full UAV stack, from embedded flight software and state estimation through mission logic and system integration, with a strong focus on reliability and practical field performance.
This position suits someone who enjoys both software development and hands-on hardware work, and who's comfortable owning problems across shifting project contexts. The ideal candidate brings solid robotics and autonomy fundamentals - estimation, controls, and coordinate frames - and is eager to grow deep expertise in the PX4 autopilot stack, including VIO, map matching, and GPS-denied navigation.
MUST HAVE PERMANENT RESIDENCE IN US
Responsibilities
  • Develop and modify PX4 or ArduPilot firmware to support new autonomy features and system capabilities
  • Implement mission logic, flight behaviors, and MAVLink integrations
  • Integrate sensors, cameras, radios, and payloads into UAV platforms
  • Tune and analyze estimation and control performance (EKF/Kalman filtering, PID loops) across platforms
  • Conduct flight testing and validate autonomous behaviors in real-world conditions
  • Analyze flight logs and telemetry to diagnose and resolve issues
  • Debug problems across the UAV stack including firmware, networking, hardware, and companion computers
  • Collaborate with engineers to design robust UAV architectures and workflows
  • Document system behavior, debugging results, and design decisions
Required Qualifications
  • Strong robotics/autonomy fundamentals: coordinate frame transformations, state estimation (Kalman filtering / sensor fusion), and PID control analysis
  • Strong C++ or C programming skills
  • Experience with PX4 or ArduPilot - or demonstrated ability to ramp quickly on a complex embedded autopilot codebase
  • Experience flying and testing UAV or robotic platforms
  • Familiarity with MAVLink, flight logs, and autopilot configuration
  • Experience debugging complex software and hardware interactions
  • Ability to work independently and take ownership of engineering tasks across multiple projects
  • Strong problem-solving skills and practical engineering judgment
Preferred Qualifications
  • Experience with ROS/ROS 2 and Nav2
  • Experience with visual-inertial odometry (VIO), map matching, or GPS-denied navigation
  • Experience building or configuring drones from components
  • Familiarity with companion computers (Linux), MAVSDK, or ROS
  • Experience integrating sensors such as LiDAR, cameras, or GPS systems
  • Experience with embedded systems or hardware debugging
  • Experience analyzing flight logs and tuning flight controllers
  • Personal robotics, UAV, or embedded systems projects
What We Value
  • Engineers who build and experiment outside of work
  • Strong ownership and accountability for outcomes
  • Practical, hands-on problem solving
  • Clear communication and collaboration within small teams
  • Curiosity and continuous learning

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