Projects
Assignments
Overview
This course offers a comprehensive understanding of localization algorithms used in autonomous driving. Participants will gain hands-on experience with state estimation techniques, sensor fusion methods, and advanced localization algorithms such as Kalman Filters (KF, EKF, UKF), lidar-based localization, and particle filters. The course emphasizes practical applications, enabling learners to develop and implement effective localization solutions for self-driving cars.
Objectives
By the end of this course, leaner will be able to:
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Understand the role and challenges of localization in autonomous driving
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Apply state estimation techniques and sensor fusion for accurate localization
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Implement Kalman Filter variants (KF, EKF, UKF) for state estimation
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Develop lidar-based localization algorithms using point cloud processing
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Implement particle filter localization for robust performance
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Perform coordinate transformations and sensor data fusion
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Evaluate and compare the performance of different localization algorithms
Prerequisites
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Python Basics
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Machine Learning (ML) Fundamentals
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Modern C++
Course Outline
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Importance of localization in autonomous driving
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Global and local localization methods
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Sensor systems: GPS, IMU, lidar, cameras, wheel odometry
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Challenges in localization: sensor noise, environmental factors, GPS outages
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Overview of state estimation and sensor fusion concepts
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Coordinate transformations and rotation matrices
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Practical exercise: Performing basic coordinate transformations using Python
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Introduction to Bayesian filtering for state estimation
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Markov process and Hidden Markov Models (HMM)
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Recursive Bayesian estimation and its applications
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Practical exercise: Implementing state estimation algorithms using Python
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Understanding the Linear Kalman Filter algorithm
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State prediction and measurement update steps
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Kalman Filter implementation for localization
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Analysis of KF assumptions and limitations
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Practical exercise: Implementing KF for vehicle tracking using simulated GPS and IMU data
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Managing nonlinearity using EKF and UKF
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EKF linearization and Jacobian matrix calculations
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UKF sigma point generation for accurate state estimation
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Comparison of KF, EKF, and UKF performance
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Practical exercise: Implementing EKF and UKF for localization in nonlinear scenarios
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Point cloud processing techniques for lidar localization
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Point cloud registration and alignment using ICP and NDT algorithms
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Lidar-based Simultaneous Localization and Mapping (SLAM)
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Practical exercise: Implementing lidar localization using simulated point cloud data
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Understanding particle filter algorithms for localization
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Importance sampling and resampling methods
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Particle filter implementation for robust localization
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Comparison of particle filter and Kalman filter approaches
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Practical exercise: Implementing particle filter localization with sensor noise and occlusions
