This repository is inspired by the lectures of Dr. Cyrill Stachniss and the concepts covered in the Sensor Fusion course. It provides a practical overview of fundamental and advanced topics in Simultaneous Localization and Mapping (SLAM), including:
The following figures illustrate concepts related to Kalman Filters (EKF, UKF, etc.):
- SLAM Fundamentals
- Kalman Filters and their variants:
- Extended Kalman Filter (EKF)
- Unscented Kalman Filter (UKF)
- Information Filters
- Particle Filters
- Graph-Based SLAM
- Least-Squares Error Minimization
- Robust Optimization Techniques
- Hierarchical Approaches
- Data Association Methods
- SLAM Front-Ends
- Appearance-Based Techniques
- Long-Term SLAM Operation
- Semantic Mapping
This repository combines theory and practical implementation examples inspired by both the Sensor Fusion course and Dr. Stachniss’ lectures to provide a solid foundation for research and application in robotics.