The Kalman Filter (KF) is a set of mathematical equations that when operating together implement a predictor-corrector type of estimator that is optimal in the sense that it minimizes the estimated error covariance when some presumed conditions are met.
Kalman Filtering can be understood as a way of making sense of a noisy world.
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In the prediction step, the Kalman filter produces estimates of the current state variables, along with their uncertainties.
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Once the outcome of the next measurement (necessarily corrupted with some amount of error, including random noise) is observed, these estimates are updated using a weighted average, with more weight being given to estimates with higher certainty.
| Ego Robot's Initial State | Ego Robot's State with Noise |
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| Ground Truth vs Estimated | Ego Robot's State with Disturbance |
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• Developed Kalman Filter-based state estimation model for precise approximation & visualization of a robot’s state.
• Attained an extremely low Mean Square Error of 2.8% & 1.1% for the robot’s position & velocity on evaluation.