Complementary Filter
A complementary filter is a signal processing technique used to combine data from multiple sensors (e.g., accelerometers and gyroscopes) to estimate orientation or position with improved accuracy and reduced noise. It works by applying a high-pass filter to one sensor's data (e.g., gyroscope for short-term stability) and a low-pass filter to another sensor's data (e.g., accelerometer for long-term stability), then summing the filtered outputs. This approach is computationally efficient and widely used in robotics, drones, and inertial measurement units (IMUs) to mitigate sensor drift and noise.
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. 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.