blob: 26d46cc007ea4e977977dcef367fa79e30560544 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
|
#pragma once
#include <Eigen/Dense>
using namespace Eigen;
class kalman_filter {
private:
VectorXf state_vector; // x
MatrixXf state_covariance; // P
MatrixXf state_transition_M; // F
MatrixXf control_input_M; // B
MatrixXf measurement_M; // H
MatrixXf process_noise_covariance; // Q
MatrixXf measurement_covariance; // R
MatrixXf I; // Identity
int n; // State Vector Dimension
int p; // Control Vector Dimension
int m; // Measurement Vector Dimension
float dt; // timestep
/**
* @brief Initialize all necessary matrices.
*
*/
void matrix_initialize();
/**
* @brief Update any existing variable elements in your State Transition
* & Control Input matrices.
*
*/
void matrix_update();
/**
* @brief Predict current State Vector & State Covariance
* given the current control input.
*
* @param control_vec The control input to be applied to the
* previous State Vector
*/
void predict(VectorXf control_vec);
/**
* @brief Correct the State Vector & State Covariance predictions
* given a related current measurement.
*
* @param measurement Current measurement
*/
void update(VectorXf measurement);
public:
kalman_filter();
/**
* @brief Construct a new Kalman Filter object
* Set the sizes of the Filter's user inputs
*
* @param state_dim State Vector Dimension. i.e. dim(x)
* @param control_dim Control/Input Vector Dimension. i.e. dim(u)
* @param measurement_dim Measurement Vector Dimension. i.e. dim(z)
* @param dt timestep
*/
kalman_filter(int state_dim, int control_dim, int measurement_dim, float dt);
/**
* @brief Optional function to set a custom initial state for the Filter.
* If not called, State Vector & State Covariance are zero-initialized
*
* @param state_vec Initial State Vector
* @param state_cov Initial State Covariance
*
* @return Whether state initialization was successful
*/
bool state_initialize(VectorXf state_vec, MatrixXf state_cov);
/**
* @brief Perform Kalman Filter operation with given control input vector
* and measurement.
*
* @param control current control command
* @param measurement current measurement
* @param dt timestep
*
* @return Filtered state vector
*/
VectorXf run(VectorXf control, VectorXf measurement, float _dt);
};
|
