Explore MATLAB source code curated for "机器人定位" with clean implementations, documentation, and examples.
New version code utilizing Extended Kalman Filter for Simultaneous Localization and Mapping (SLAM), primarily designed for robotic positioning and automated map construction with enhanced algorithmic implementations.
This MATLAB program demonstrates robot tracking through particle filtering. WiFi measurements are simulated using a ray-tracing engine that accommodates up to 3 wall reflections. The particle filter algorithm corrects distance estimates and trajectory calculations.
Extended Kalman Filter MATLAB simulation for robotic applications including localization, mapping, and SLAM with enhanced algorithm implementation details
Implementation of Particle Filter Algorithm in Robot Localization with Complete Source Code and Technical Documentation
Implementation of robot localization and mapping using Extended Kalman Filter (EKF) for SLAM applications, featuring sensor fusion and environmental feature tracking
Implementation of robot localization and map building using Kalman filter (SLAM) with detailed simulations for 1D, 2D, and 3D scenarios, including code structure and algorithmic approaches