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Update factory calibration of WMR headsets with wmr2bslt tool

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Files changed (4) hide show
  1. M_monado_datasets/MG_reverb_g2/extras/calibration.factory.json +1 -1
  2. M_monado_datasets/MO_odyssey_plus/extras/calibration.factory.json +1 -0
  3. M_monado_datasets/MO_odyssey_plus/extras/factory.json +209 -741
  4. M_monado_datasets/extras/wmr2bslt_calib.py +261 -0
M_monado_datasets/MG_reverb_g2/extras/calibration.factory.json CHANGED
@@ -1,6 +1,6 @@
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- "comment": "Calibration as obtained from transfroming factory.json data from the headset into Basalt's calibration format",
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  "px": -0.052326593548059466,
 
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  {
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  "px": -0.052326593548059466,
M_monado_datasets/MO_odyssey_plus/extras/calibration.factory.json CHANGED
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  }
M_monado_datasets/extras/wmr2bslt_calib.py ADDED
@@ -0,0 +1,261 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+
3
+ # Converts Windows Mixed Reality (Reverb G2 and Odyssey+) calibrations in to
4
+ # basalt calibrations.
5
+
6
+ # Run with ./wmr2bslt_calib.py your_wmrcalib.json > your_calib.json
7
+
8
+ import json
9
+ import argparse
10
+ import numpy as np
11
+ from numpy.linalg import inv
12
+
13
+ from math import sqrt
14
+
15
+
16
+ def get(j, name):
17
+ assert name in ["HT0", "HT1", "Gyro", "Accelerometer"]
18
+ is_imu = name in ["Gyro", "Accelerometer"]
19
+ calib = j["CalibrationInformation"]
20
+ sensors = calib["InertialSensors" if is_imu else "Cameras"]
21
+ name_key = "SensorType" if is_imu else "Location"
22
+ sensor = next(filter(lambda s: s[name_key].endswith(name), sensors))
23
+ return sensor
24
+
25
+
26
+ def rt2mat(rt):
27
+ R33 = np.array(rt["Rotation"]).reshape(3, 3)
28
+ t31 = np.array(rt["Translation"]).reshape(3, 1)
29
+ T34 = np.hstack((R33, t31))
30
+ T44 = np.vstack((T34, [0, 0, 0, 1]))
31
+ return T44
32
+
33
+
34
+ def rmat2quat(r):
35
+ w = sqrt(1 + r[0, 0] + r[1, 1] + r[2, 2]) / 2
36
+ w4 = 4 * w
37
+ x = (r[2, 1] - r[1, 2]) / w4
38
+ y = (r[0, 2] - r[2, 0]) / w4
39
+ z = (r[1, 0] - r[0, 1]) / w4
40
+ return np.array([x, y, z, w])
41
+
42
+
43
+ def project(intrinsics, x, y, z):
44
+ fx, fy, cx, cy, k1, k2, k3, k4, k5, k6, p1, p2 = (
45
+ intrinsics["fx"],
46
+ intrinsics["fy"],
47
+ intrinsics["cx"],
48
+ intrinsics["cy"],
49
+ intrinsics["k1"],
50
+ intrinsics["k2"],
51
+ intrinsics["k3"],
52
+ intrinsics["k4"],
53
+ intrinsics["k5"],
54
+ intrinsics["k6"],
55
+ intrinsics["p1"],
56
+ intrinsics["p2"],
57
+ )
58
+
59
+ xp = x / z
60
+ yp = y / z
61
+ r2 = xp * xp + yp * yp
62
+ cdist = (1 + r2 * (k1 + r2 * (k2 + r2 * k3))) / (
63
+ 1 + r2 * (k4 + r2 * (k5 + r2 * k6))
64
+ )
65
+ deltaX = 2 * p1 * xp * yp + p2 * (r2 + 2 * xp * xp)
66
+ deltaY = 2 * p2 * xp * yp + p1 * (r2 + 2 * yp * yp)
67
+ xpp = xp * cdist + deltaX
68
+ ypp = yp * cdist + deltaY
69
+ u = fx * xpp + cx
70
+ v = fy * ypp + cy
71
+ return u, v
72
+
73
+
74
+ def extrinsics(j, cam):
75
+ # NOTE: The `Rt` field seems to be a transform from the sensor to HT0 (i.e.,
76
+ # from HT0 space to sensor space). For basalt we need the transforms
77
+ # expressed w.r.t IMU origin.
78
+
79
+ # NOTE: The gyro and magnetometer translations are 0, probably because an
80
+ # HMD is a rigid body. Therefore the accelerometer is considered as the IMU
81
+ # origin.
82
+
83
+ imu = get(j, "Accelerometer")
84
+ T_i_c0 = rt2mat(imu["Rt"])
85
+
86
+ T = None
87
+ if cam == "HT0":
88
+ T = T_i_c0
89
+ elif cam == "HT1":
90
+ cam1 = get(j, "HT1")
91
+ T_c1_c0 = rt2mat(cam1["Rt"])
92
+ T_c0_c1 = inv(T_c1_c0)
93
+ T_i_c1 = T_i_c0 @ T_c0_c1
94
+ T = T_i_c1
95
+ else:
96
+ assert False
97
+
98
+ q = rmat2quat(T[0:3, 0:3])
99
+ p = T[0:3, 3]
100
+ return {
101
+ "px": p[0],
102
+ "py": p[1],
103
+ "pz": p[2],
104
+ "qx": q[0],
105
+ "qy": q[1],
106
+ "qz": q[2],
107
+ "qw": q[3],
108
+ }
109
+
110
+
111
+ def resolution(j, cam):
112
+ camera = get(j, cam)
113
+ width = camera["SensorWidth"]
114
+ height = camera["SensorHeight"]
115
+ return [width, height]
116
+
117
+
118
+ def intrinsics(j, cam):
119
+ # https://github.com/microsoft/Azure-Kinect-Sensor-SDK/blob/2feb3425259bf803749065bb6d628c6c180f8e77/include/k4a/k4atypes.h#L1024-L1046
120
+ camera = get(j, cam)
121
+ model_params = camera["Intrinsics"]["ModelParameters"]
122
+ assert (
123
+ camera["Intrinsics"]["ModelType"]
124
+ == "CALIBRATION_LensDistortionModelRational6KT"
125
+ )
126
+ width = camera["SensorWidth"]
127
+ height = camera["SensorHeight"]
128
+ return {
129
+ "camera_type": "pinhole-radtan8",
130
+ "intrinsics": {
131
+ "fx": model_params[2] * width,
132
+ "fy": model_params[3] * height,
133
+ "cx": model_params[0] * width,
134
+ "cy": model_params[1] * height,
135
+ "k1": model_params[4],
136
+ "k2": model_params[5],
137
+ "p1": model_params[13],
138
+ "p2": model_params[12],
139
+ "k3": model_params[6],
140
+ "k4": model_params[7],
141
+ "k5": model_params[8],
142
+ "k6": model_params[9],
143
+ "rpmax": model_params[14],
144
+ },
145
+ }
146
+
147
+
148
+ def view_offset(j):
149
+ """
150
+ This is a very rough offset in pixels between the two cameras. Originally we
151
+ needed to manually estimate it like explained and shown here
152
+ https://youtu.be/jyQKjyRVMS4?t=670.
153
+ With this calculation we get a similar number without the need to open Gimp.
154
+
155
+ In reality this offset changes based on distance to the point, nonetheless
156
+ it helps to get some features tracked in the right camera.
157
+ """
158
+
159
+ # Rough approximation of how far from the cameras features will likely be in your room
160
+ DISTANCE_TO_WALL = 2 # In meters
161
+
162
+ cam1 = get(j, "HT1")
163
+ width = cam1["SensorWidth"]
164
+ height = cam1["SensorHeight"]
165
+ cam1_intrinsics = intrinsics(j, "HT1")["intrinsics"]
166
+ T_c1_c0 = rt2mat(cam1["Rt"]) # Maps a point in c0 space to c1 space
167
+ p = np.array([0, 0, DISTANCE_TO_WALL, 1]) # Fron tof c0, in homogeneous coords
168
+ p_in_c1 = T_c1_c0 @ p # Point in c1 coordinates
169
+ u, v = project(cam1_intrinsics, *p_in_c1[0:3])
170
+ view_offset = [width / 2 - u, height / 2 - v] # We used a point in the middle of c0
171
+ return view_offset
172
+
173
+
174
+ def calib_accel_bias(j):
175
+ # https://github.com/microsoft/Azure-Kinect-Sensor-SDK/blob/2feb3425259bf803749065bb6d628c6c180f8e77/include/k4ainternal/calibration.h#L48-L77
176
+ # https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1CalibAccelBias.html#details
177
+ # https://gitlab.com/VladyslavUsenko/basalt-headers/-/issues/8
178
+ accel = get(j, "Accelerometer")
179
+ bias = accel["BiasTemperatureModel"]
180
+ align = accel["MixingMatrixTemperatureModel"]
181
+ return [
182
+ -bias[0 * 4],
183
+ -bias[1 * 4],
184
+ -bias[2 * 4],
185
+ align[0 * 4] - 1, # [0, 0]
186
+ align[3 * 4], # [1, 0]
187
+ align[6 * 4], # [2, 0]
188
+ align[4 * 4] - 1, # [1, 1]
189
+ align[7 * 4], # [2, 1]
190
+ align[8 * 4] - 1, # [2, 2]
191
+ ]
192
+
193
+
194
+ def calib_gyro_bias(j):
195
+ # https://github.com/microsoft/Azure-Kinect-Sensor-SDK/blob/2feb3425259bf803749065bb6d628c6c180f8e77/include/k4ainternal/calibration.h#L48-L77
196
+ # https://vladyslavusenko.gitlab.io/basalt-headers/classbasalt_1_1CalibGyroBias.html#details
197
+ gyro = get(j, "Gyro")
198
+ bias = gyro["BiasTemperatureModel"]
199
+ align = gyro["MixingMatrixTemperatureModel"]
200
+ return [
201
+ -bias[0 * 4],
202
+ -bias[1 * 4],
203
+ -bias[2 * 4],
204
+ align[0 * 4] - 1, # [0, 0]
205
+ align[3 * 4], # [1, 0]
206
+ align[6 * 4], # [2, 0]
207
+ align[1 * 4], # [0, 1]
208
+ align[4 * 4] - 1, # [1, 1]
209
+ align[7 * 4], # [2, 1]
210
+ align[2 * 4], # [0, 2]
211
+ align[5 * 4], # [1, 2]
212
+ align[8 * 4] - 1, # [2, 2]
213
+ ]
214
+
215
+
216
+ def noise_std(j, name):
217
+ imu = get(j, name)
218
+ return imu["Noise"][0:3]
219
+
220
+
221
+ def bias_std(j, name):
222
+ imu = get(j, name)
223
+ return list(map(sqrt, imu["BiasUncertainty"]))
224
+
225
+
226
+ def main():
227
+ parser = argparse.ArgumentParser()
228
+ parser.add_argument("wmr_json_file", help="Input WMR json calibration file")
229
+ args = parser.parse_args()
230
+ in_fn = args.wmr_json_file
231
+
232
+ with open(in_fn) as f:
233
+ j = json.load(f)
234
+
235
+ # We get 250 packets with 4 samples each per second, totalling 1000 samples per second.
236
+ # But in monado we just average those 4 samples to reduce the noise. So we have 250hz.
237
+ IMU_UPDATE_RATE = 250
238
+
239
+ out_calib = {
240
+ "value0": {
241
+ "T_imu_cam": [extrinsics(j, "HT0"), extrinsics(j, "HT1")],
242
+ "intrinsics": [intrinsics(j, "HT0"), intrinsics(j, "HT1")],
243
+ "resolution": [resolution(j, "HT0"), resolution(j, "HT1")],
244
+ "calib_accel_bias": calib_accel_bias(j),
245
+ "calib_gyro_bias": calib_gyro_bias(j),
246
+ "imu_update_rate": IMU_UPDATE_RATE,
247
+ "accel_noise_std": noise_std(j, "Accelerometer"),
248
+ "gyro_noise_std": noise_std(j, "Gyro"),
249
+ "accel_bias_std": bias_std(j, "Accelerometer"),
250
+ "gyro_bias_std": bias_std(j, "Gyro"),
251
+ "cam_time_offset_ns": 0,
252
+ # "view_offset": view_offset(j),
253
+ "vignette": [],
254
+ }
255
+ }
256
+
257
+ print(json.dumps(out_calib, indent=4))
258
+
259
+
260
+ if __name__ == "__main__":
261
+ main()