1616
1717
1818# Simulation parameter
19- Qsim = np .diag ([0.2 , math .radians (1 .0 )])** 2
19+ Qsim = np .diag ([0.0 , math .radians (0 .0 )])** 2
2020Rsim = np .diag ([1.0 , math .radians (10.0 )])** 2
2121
2222DT = 0.1 # time tick [s]
@@ -100,6 +100,8 @@ def add_new_lm(particle, z):
100100
101101 particle .lm [lm_id , 0 ] = particle .x + r * c
102102 particle .lm [lm_id , 1 ] = particle .y + r * s
103+ # print(particle.lm)
104+ # print(lm_id)
103105
104106 # covariance
105107 Gz = np .matrix ([[c , - r * s ],
@@ -110,11 +112,17 @@ def add_new_lm(particle, z):
110112 return particle
111113
112114
115+ def feature_update (particle , z , R ):
116+
117+ return particle
118+
119+
113120def compute_weight (particle , z ):
114121
115122 lm_id = int (z [0 , 2 ])
116123
117124 lmxy = np .matrix (particle .lm [lm_id , :])
125+ print (lmxy )
118126
119127 # calc landmark xy
120128 r = z [0 , 0 ]
@@ -154,7 +162,8 @@ def update_with_observation(particles, z):
154162 else :
155163 w = compute_weight (particles [ip ], z [iz , :]) # w = p(z_k | x_k)
156164 particles [ip ].w = particles [ip ].w + w
157- # particles(i)= feature_update(particles(i), zf, idf, R)
165+ particles [ip ] = feature_update (
166+ particles [ip ], z [iz , :], Cx [0 :2 , 0 :2 ])
158167
159168 return particles
160169
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