Implement BayesFilter base class and refactor localization filters (#1277)#1307
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ProblemShooter wants to merge 3 commits intoAtsushiSakai:masterfrom
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Implement BayesFilter base class and refactor localization filters (#1277)#1307ProblemShooter wants to merge 3 commits intoAtsushiSakai:masterfrom
ProblemShooter wants to merge 3 commits intoAtsushiSakai:masterfrom
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This commit implements a base BayesFilter abstract class to standardize the interface across all Bayesian filtering algorithms in the Localization directory. Key changes: - Created bayes_filter.py with abstract BayesFilter base class - Defines common interface methods: predict(), update(), get_state(), get_covariance() - All filter implementations (EKF, UKF, PF, etc.) will inherit from this base class - Provides consistent API for all localization filters - Includes comprehensive docstrings This addresses issue AtsushiSakai#1277 by creating a unified structure for filter implementations, making the code more maintainable and consistent across different filter types. Reference: AtsushiSakai#1277
…kai#1277) This commit implements an Extended Kalman Filter class that inherits from the BayesFilter base class, demonstrating the unified interface pattern. Key features: - ExtendedKalmanFilter extends BayesFilter - Implements all required abstract methods: predict(), update(), get_state(), get_covariance() - Maintains Gaussian belief with state (mean) and covariance - Includes motion and observation model methods with Jacobians - Provides configurable noise covariances (Q and R) - Comprehensive documentation and docstrings This demonstrates the consistent API for EKF localization, making it easier to swap between different filter implementations. Addresses issue AtsushiSakai#1277 - Code improvement in Localization directory.
This commit implements a Particle Filter class that inherits from the BayesFilter base class, demonstrating the unified interface pattern for non-Gaussian localization. Key features: - ParticleFilter extends BayesFilter - Implements all required abstract methods: predict(), update(), get_state(), get_covariance() - Represents belief as weighted particles for non-Gaussian distributions - Includes low-variance resampling algorithm - Provides effective particle count calculation - Motion and observation models with configurable noise - Comprehensive documentation and docstrings This demonstrates the consistent API for Particle Filter localization, allowing easy integration with other filter types. Addresses issue AtsushiSakai#1277 - Code improvement in Localization directory.
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Reference issue
Fixes #1277
What does this implement/fix?
This PR implements a base BayesFilter class and refactors the Localization directory structure to create a unified, consistent API across all Bayesian filtering algorithms.
Key Changes:
Base BayesFilter Class (
bayes_filter.py)predict(),update(),get_state(),get_covariance()reset()method that can be overriddenExtended Kalman Filter Implementation (
ekf_localization.py)Particle Filter Implementation (
pf_localization.py)Benefits:
Additional information
This implementation demonstrates the design pattern requested in issue #1277. The existing filter implementations in subdirectories remain unchanged for backward compatibility. The new class-based implementations can coexist with the existing script-style implementations.
Future work could involve:
CheckList