SLAM Implementation Using EKF and Particle Filter Algorithms

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Resource Overview

SLAM has been widely applied in robotics and drones. This program implements SLAM using Extended Kalman Filter (EKF) and Particle Filter methods, serving as an educational resource for learning autonomous navigation systems.

Detailed Documentation

SLAM (Simultaneous Localization and Mapping) has been successfully implemented in robotics and drone applications. This educational program demonstrates SLAM implementation using Extended Kalman Filter (EKF) and Particle Filter algorithms. The codebase provides practical examples of how these algorithms enhance positioning accuracy and mapping quality in unknown environments. Key implementation aspects include: EKF-based state estimation for Gaussian noise environments, particle filter implementation for non-linear systems using importance sampling, and sensor data fusion techniques for landmark association. SLAM technology enables robots and drones to perform real-time localization and navigation while constructing environmental maps, making it a fundamental technology in autonomous systems. Mastery of SLAM algorithms is crucial for advancing research and applications in robotics and unmanned aerial vehicles.

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