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Vehicle State Estimation using Error-State Extended Kalman Filter
| Date | Stars |
|---|---|
| 2026-07-24 | 271 |
| 2026-07-25 | 271 |
| 2026-07-28 | 271 |
| 2026-07-30 | 271 |
| 2026-08-06 | 271 |
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# Error-State-Extended-Kalman-Filter Vehicle State Estimation using Error-State Extended Kalman Filter <p> In this project, I implemented the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. </p> - The data set contains measurements from a sensor array on a moving self-driving car. - The sensor array consists of an IMU, a GNSS receiver, and a LiDAR, all of which provide measurements of varying reliability and at different rates. - Our goal is to implement a state estimator that fuses the available sensor measurements to provide a reasonable estimate of the vehicle's pose and velocity. Specifically, we will be implementing the Error-State Extended Kalman Filter. - In the main filter loop, you will first update the state and the uncertainty using IMU readings. - Whenever a GNSS or LiDAR measurement becomes available, you will execute the appropriate common gain computation, error state, and covariance updates. - We will have access to the actual pose and velocity values from Carla for a large section of the trajectory. So you will be able to compare your trajectory estimates to the ground truth data. ## Solution Approach           # Running - To run es_ekf.py, simply call **python es_ekf.py** from the command line or 'run es_ekf.py' from within an interactive shell. - As the code runs, some visualizations (plots) will already appear for you, including a plot of the ground truth trajectory, a plot of the ground truth trajectory compared to your estimated trajectory, and six error plots. # Final Notes: - This is a module assignment project from State Estimation and Localization course of **Self-Driving Cars Specialization by University of Toronto.** - All of the data from Carla Simulator contained as the Python pickle (pkl) file inside the Data folder - The data folder contains the data you will use for the project, and the rotations.py file contains a Quaternion class
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6df01b89fe93bcef, topic:autonomous-driving, topic:self-driving-car, readme:self-driving