Bayes++ Library入门学习之熟悉UKF相关类
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UKF-SLAM是一种比较流行SLAM方案。相比EKF-SLAM,UKF利用unscented transform代替了EKF的线性化趋近,因而具有更高的精度。Bayes++库中的unsFlt.hpp中给出了UKF实现的相关类。
namespace Bayesian_filter 39 { 40 41 class Unscented_predict_model : public Predict_model_base 42 /* Specific Unscented prediction model for Additive noise 43 * x(k|k-1) = f(x(k-1|k-1)) + w(x(k)) 44 * 45 * Unscented filter requires 46 * f the function part of the non-linear model 47 * Q the covariance of the additive w(x(k)), w is specifically allow to be a function of state 48 */ 49 { 50 public: 51 Unscented_predict_model (std::size_t q_size) 52 { 53 q_unscented = q_size; 54 } 55 56 virtual const FM::Vec& f(const FM::Vec& x) const = 0; 57 // Functional part of additive model 58 // Note: Reference return value as a speed optimisation, MUST be copied by caller. 59 60 virtual const FM::SymMatrix& Q(const FM::Vec& x) const = 0; 61 // Covariance of additive noise 62 // Note: Reference return value as a speed optimisation, MUST be copied by caller. 63 private: 64 friend class Unscented_filter; // Filter implementation need to know noise size 65 std::size_t q_unscented; 66 }; 67 68 69 class Unscented_scheme : public Linrz_kalman_filter, public Functional_filter 70 { 71 private: 72 std::size_t q_max; // Maximum size allocated for noise model, constructed before XX 73 public: 74 FM::ColMatrix XX; // Unscented form of state, with associated Kappa 75 Float kappa; 76 77 Unscented_scheme (std::size_t x_size, std::size_t z_initialsize = 0); 78 Unscented_scheme& operator= (const Unscented_scheme&); 79 // Optimise copy assignment to only copy filter state 80 81 void init (); 82 void init_XX (); 83 void update (); 84 void update_XX (Float kappa); 85 86 void predict (Unscented_predict_model& f); 87 // Efficient Unscented prediction 88 void predict (Functional_predict_model& f); 89 void predict (Additive_predict_model& f); 90 Float predict (Linrz_predict_model& f) 91 { // Adapt to use the more general additive model 92 predict(static_cast<Additive_predict_model&>(f)); 93 return 1.; // Always well condition for additive predict 94 } 95 96 Float observe (Uncorrelated_additive_observe_model& h, const FM::Vec& z); 97 Float observe (Correlated_additive_observe_model& h, const FM::Vec& z); 98 // Unscented filter implements general additive observe models 99 100 Float observe (Linrz_uncorrelated_observe_model& h, const FM::Vec& z) 101 { // Adapt to use the more general additive model 102 return observe (static_cast<Uncorrelated_additive_observe_model&>(h),z); 103 } 104 Float observe (Linrz_correlated_observe_model& h, const FM::Vec& z) 105 { // Adapt to use the more general additive model 106 return observe (static_cast<Correlated_additive_observe_model&>(h),z); 107 } 108 109 public: // Exposed Numerical Results 110 FM::Vec s; // Innovation 111 FM::SymMatrix S, SI; // Innovation Covariance and Inverse 112 113 protected: 114 virtual Float predict_Kappa (std::size_t size) const; 115 virtual Float observe_Kappa (std::size_t size) const; 116 /* Unscented Kappa values 117 default uses the rule which minimise mean squared error of 4th order term 118 */ 119 120 protected: // allow fast operation if z_size remains constant 121 std::size_t last_z_size; 122 void observe_size (std::size_t z_size); 123 124 private: 125 void unscented (FM::ColMatrix& XX, const FM::Vec& x, const FM::SymMatrix& X, Float scale); 126 /* Determine Unscented points for a distribution */ 127 std::size_t x_size; 128 std::size_t XX_size; // 2*x_size+1 129 130 protected: // Permanently allocated temps 131 FM::ColMatrix fXX; 132 }; 133 134 135 }//namespace 136 #endif
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