Description
Sensor Fusion for Robotics: IMU, Kalman Filters & EKF. This course involves the use of artificial intelligence. Every robot faces the same problem; the inertial measurement unit (IMU) updates hundreds of times per second but drifts within a minute, the GPS knows an approximate position but jumps along the way, and the wheel encoders are smooth and accurate until the wheel slips. None of them are reliable on their own, yet the robot must make a definitive decision about its position many times per second and continuously. Sensor fusion is the method that produces this response, and this course implements its structure from the ground up without hiding formulas in ready-made libraries.
In this course, the student first learns how to troubleshoot each sensor, as bias, noise, and drift are three different errors that require different responses. Then, the Kalman filter is implemented first in one dimension, then in two dimensions, and finally in the full robot case, along with the Extended Kalman Filter (EKF) for nonlinear cases. Along the way, real-world challenges such as GPS outages in a tunnel, varying sensor delays, and tuning covariance matrices are explored. Interactive browser labs and line-by-line hands-on work in VS Code help deepen learning. In the final project, real data from a yard robot with all the challenging errors is processed to build a robust estimator. The only prerequisite for this course is familiarity with Python, numpy, and matplotlib.
What you will learn
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Implementing Kalman Filter from Scratch in Python Instead of Using Ready-Made Libraries
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Filter extension to EKF for nonlinear motions and angle and distance measurements with manual Jacobian differentiation
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Integrate data from IMU, GPS, encoders, and markers at different rates and latencies
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Detecting triple sensor errors including noise, bias, and time-consuming drift
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Reading the covariance matrix as a geometric shape and understanding robot blind spots
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Fine-tune the Q and R matrices and identify signs of failure due to their incorrect adjustment.
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Managing GPS uncertainty and preserving true uncertainty instead of definitive but incorrect answers
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Multipath reflection outlier removal and correction error measurement
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Troubleshooting divergent filters and evaluating estimators using NIS and covariance consistency indices
This course is suitable for people who:
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Robotics engineers who use filter libraries but lack the ability to troubleshoot drifting filters
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Students and programmers who have read Kalman equations and are seeking a deeper understanding of them
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Designers of drones, rovers, and autonomous mobile robots (AMR) who need to manage their robot’s positioning
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Machine vision and SLAM specialists who need a state estimation layer to underpin their system
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Anyone looking to learn how to actually set up Q and R matrices
Course details Sensor Fusion for Robotics: IMU, Kalman Filters & EKF
- Publisher: Udemy
- Instructor: Frank Robotics Lab
- Training level: Beginner to advanced
- Training duration: 8 hours and 16 minutes
- Number of lessons: 89
Course headings
Prerequisites for the Sensor Fusion for Robotics: IMU, Kalman Filters & EKF course
- Python you can read and write: functions, loops, numpy arrays, and the ability to read a traceback
- School level algebra and comfort with the idea of a matrix; the statistics is taught from scratch
- A laptop of any age. No GPU, no CUDA, no ROS, no simulator and no cloud account
- numpy and matplotlib, which the first section installs with you
- No prior Kalman filter knowledge at all. That is the point of the course
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 1080p
Download link
Rapidgator link
File(s) password: www.downloadly.ir
File size
10.5 GB