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Computer Science
Robotics
Robotics and Autonomous Systems
1. Introduction to Robotics and Autonomous Systems
2. Robot Kinematics and Dynamics
3. Sensors and Perception
4. Localization and State Estimation
5. Planning and Navigation
6. Robot Control
7. Machine Learning for Robotics
8. System Integration and Implementation
9. Safety, Reliability, and Ethics
10. Applications and Case Studies
Localization and State Estimation
Localization Problem Formulation
Global Localization
Position Tracking
Kidnapped Robot Problem
Multi-hypothesis Tracking
Probabilistic State Estimation
Uncertainty Modeling
Belief State Representation
Bayes Filter Framework
Recursive State Estimation
Kalman Filtering
Linear Kalman Filter
State Prediction
Measurement Update
Covariance Propagation
Extended Kalman Filter
Linearization Process
Jacobian Computation
EKF Limitations
Unscented Kalman Filter
Sigma Point Selection
Unscented Transform
UKF Advantages
Information Filter
Particle Filtering
Monte Carlo Methods
Particle Representation
Importance Sampling
Resampling Strategies
Systematic Resampling
Stratified Resampling
Particle Degeneracy
Adaptive Particle Filters
Mapping Techniques
Map Representation Methods
Occupancy Grid Maps
Grid Resolution
Probability Updates
Feature-based Maps
Point Landmarks
Line Features
Geometric Primitives
Topological Maps
Semantic Maps
Map Building Algorithms
Occupancy Grid Mapping
Feature Extraction and Mapping
Map Merging
Simultaneous Localization and Mapping
SLAM Problem Formulation
Online SLAM
Full SLAM
Data Association Problem
Loop Closure Detection
Filter-based SLAM
EKF SLAM
State Augmentation
Computational Complexity
FastSLAM
Rao-Blackwellized Particle Filter
Tree-based Data Structures
Graph-based SLAM
Pose Graph Representation
Graph Optimization
Robust Estimation
Visual SLAM
Feature-based Visual SLAM
Direct Visual SLAM
RGB-D SLAM
LiDAR SLAM
Scan Matching Algorithms
Point Cloud Registration
ICP Algorithm
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3. Sensors and Perception
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5. Planning and Navigation