Kalman Filter vs Complementary Filter

Developers should learn Kalman filtering when working on applications involving real-time state estimation, sensor fusion, or tracking in fields like robotics, autonomous vehicles, and aerospace meets developers should learn and use complementary filters when building systems that require real-time orientation estimation from noisy sensor data, such as in robotics, drones, or virtual reality applications. Here's our take.

Kalman Filter

Nice Pick

Developers should learn Kalman filtering when working on applications involving real-time state estimation, sensor fusion, or tracking in fields like robotics, autonomous vehicles, and aerospace

Pros

• It is particularly useful for handling noisy sensor data, such as GPS, IMU, or lidar readings, to improve accuracy in position, velocity, or orientation estimates
• Related to: state-estimation, sensor-fusion

Cons

• Specific tradeoffs depend on your use case

Complementary Filter

Developers should learn and use complementary filters when building systems that require real-time orientation estimation from noisy sensor data, such as in robotics, drones, or virtual reality applications

Pros

• It is particularly valuable in scenarios where computational resources are limited, as it provides a simpler and faster alternative to more complex algorithms like Kalman filters, while still offering good performance for many practical use cases
• Related to: sensor-fusion, kalman-filter

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Kalman Filter if: You want it is particularly useful for handling noisy sensor data, such as gps, imu, or lidar readings, to improve accuracy in position, velocity, or orientation estimates and can live with specific tradeoffs depend on your use case.

Use Complementary Filter if: You prioritize it is particularly valuable in scenarios where computational resources are limited, as it provides a simpler and faster alternative to more complex algorithms like kalman filters, while still offering good performance for many practical use cases over what Kalman Filter offers.

The Bottom Line

Kalman Filter wins

Developers should learn Kalman filtering when working on applications involving real-time state estimation, sensor fusion, or tracking in fields like robotics, autonomous vehicles, and aerospace