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Optimizing Pruning Parameters Using Kalman Filter Analysis

This document analyzes single and double pruning results using a radius of 50m and a specified threshold. The focus is on using the Kalman Filter to produce accurate measurement values even in the presence of noise. The Kalman Filter employs the state transition model, control-input model, and includes process noise variables. It systematically removes frames where the discrepancy between the Kalman output and observations exceeds a defined threshold, thereby improving the quality of the results. The effectiveness of this method is demonstrated in pruning optimization.

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Optimizing Pruning Parameters Using Kalman Filter Analysis

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  1. Accurate Video Localization Neil Gealy 7/20/10

  2. Pruning Plots We need to analyze these plots and try to find optimal pruning parameters. Single Pruning Results – Radius: 50m Threshold: 10 Double Pruning Results – Radius: Threshold:

  3. Kalman Filter • Its purpose is to use measurements that are observed over time that contain noise and other inaccuracies, and produce values that tend to be closer to the true values of the measurements Fk is the state transition model which is applied to the previous state xk−1 Bk is the control-input model which is applied to the control vector uk wk is the process noise. zk is the measurement of the true state Hk is the observation vk is the observation noise with covariance Rk.

  4. Kalman Filter

  5. Kalman Filter When the difference between the Kalman output and the observation was greater than a threshold, I removed the frame. Difference exceeds threshold so remove this frame

  6. Results of Kalman Filter Orange dots were removed by Kalman Filter method previously described using .00009 threshold.

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