FastSLAM: Also Known as Rao-Blackwellized SLAM Method

FastSLAM (Fast Simultaneous Localization and Mapping) is an efficient method for real-time localization and mapping that integrates Rao-Blackwellized particle filtering. Its core concept decomposes the SLAM problem into two components: path estimation and map construction. This approach significantly reduces computational complexity, making it suitable for real-time navigation of mobile robots in unknown environments.

Differences Between FastSLAM 1.0 and 2.0: FastSLAM 1.0: Utilizes a simple proposal distribution (e.g., motion model), which may lead to particle degeneracy issues, particularly exhibiting instability in high-noise environments. FastSLAM 2.0: Enhances the proposal distribution by incorporating observation data to generate more reasonable particle distributions, substantially improving localization accuracy and robustness, representing a more mature version.

Key MATLAB Implementation Points: Particle Filter Framework: Maintains robot pose hypotheses through a set of particles, with each particle independently managing a local map. Map Update: Based on Rao-Blackwellization, map features are updated analytically (e.g., using Extended Kalman Filter) under known path conditions. Resampling Strategy: Prevents particle weight degeneracy by ensuring high-weight particles are retained through systematic or residual resampling techniques.

Application Scenarios: Suitable for robotic platforms with limited computational resources, such as indoor cleaning robots or drones, though further optimization is required for dynamic environments and large-scale scenarios.

(Note: Source code analysis requires consideration of actual code structure; only principle-level differences are summarized here.)