navigation-sensor-calibration: add development session inputs (PR #844)

.gitattributes

@@ -179,3 +179,4 @@ tumor-area-selection/input/slide04.tif filter=lfs diff=lfs merge=lfs -text
 tumor-area-selection/input/slide03.tif filter=lfs diff=lfs merge=lfs -text
 tumor-area-selection/input/slide01.tif filter=lfs diff=lfs merge=lfs -text
 tumor-area-selection/input/slide09.tif filter=lfs diff=lfs merge=lfs -text
+navigation-sensor-calibration/input/development/imu_noise_record.csv filter=lfs diff=lfs merge=lfs -text

navigation-sensor-calibration/input/development/gps.csv

@@ -0,0 +1,588 @@
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navigation-sensor-calibration/input/development/imu_noise_record.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2853a3e2821491495d244509eaa3e9bec8319608b22ba71a84ee90a2b5babc94
+size 17614200

navigation-sensor-calibration/input/development/measurement_model.json

@@ -0,0 +1,90 @@
+{
+  "coordinate_frames": {
+    "world": "local ENU: x east, y north, z up",
+    "body": "x forward, y left, z up",
+    "survey_pose": "sparse total-station/attitude-survey observations of the body origin; quaternion xyzw maps body vectors into ENU; gaps are intentional and must be bridged with onboard sensors"
+  },
+  "clock_model": {
+    "equation": "reference_time_s = sensor_timestamp_s - time_offset_sec - clock_drift_sec_per_sec * (sensor_timestamp_s - clock_reference_sensor_timestamp_s)",
+    "clock_reference_sensor_timestamp_s": 60.0,
+    "imu_clock": "IMU timestamps are already in the reference clock"
+  },
+  "quaternion_convention": "Hamilton xyzw; rotations are active maps between the frames named in each field",
+  "survey_model": {
+    "position_noise": "independent zero-mean Gaussian in local ENU",
+    "position_covariance_enu_m2": [
+      [
+        2.5e-07,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        2.5e-07,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        2.5e-07
+      ]
+    ],
+    "attitude_noise": "independent zero-mean Gaussian right-multiplicative rotation vector expressed in the body-frame tangent space",
+    "attitude_covariance_body_rad2": [
+      [
+        2.5e-09,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        2.5e-09,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        2.5e-09
+      ]
+    ]
+  },
+  "gps_model": "gps_position_enu = body_position_enu(reference_time) + R_enu_from_body(reference_time) @ gps_lever_arm_body_m + gps_position_bias_enu_m + noise_or_fault",
+  "gps_nominal_noise": {
+    "distribution": "independent zero-mean Gaussian in ENU",
+    "covariance_enu_m2": [
+      [
+        0.0042250000000000005,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0042250000000000005,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.010000000000000002
+      ]
+    ]
+  },
+  "imu_model": {
+    "temperature_reference_c": 25.0,
+    "specific_force": "f_body = R_body_from_imu @ C_accel @ (accel_measured - imu_accel_bias_m_s2 - imu_accel_temp_coeff_m_s2_per_c * (temperature_c - temperature_reference_c))",
+    "angular_rate": "omega_body = R_body_from_imu @ diag(imu_gyro_correction_scale) @ (gyro_measured - imu_gyro_bias_rad_s - imu_gyro_temp_coeff_rad_s_per_c * (temperature_c - temperature_reference_c))",
+    "accel_correction_packing": "[C00,C01,C02,C11,C12,C22] defines an upper-triangular C_accel with positive diagonal",
+    "dynamic_truth": "f_body = R_body_from_enu @ (d2_position_enu/dt2 - [0,0,-9.80665]); omega_body is the body-frame angular velocity of R_enu_from_body",
+    "static_truth": "expected_f*_body_m_s2 is the known body-frame specific force; true angular rate is zero",
+    "stochastic_record": "imu_noise_record.csv contains zero-mean stationary residuals after deterministic calibration; each axis is the sum of independent sampled white noise and a stationary first-order Gauss-Markov bias",
+    "stochastic_discrete_model": "b[k+1] = exp(-dt/tau) * b[k] + eta[k], with stationary standard deviation imu_*_bias_process_std and eta chosen for stationarity; residual[k] = b[k] + white[k]",
+    "white_density_convention": "the independent white sample standard deviation equals imu_*_white_noise_density / sqrt(2*dt), where dt is the timestamp spacing and density has units per sqrt(Hz)"
+  },
+  "lidar_model": {
+    "extrinsic": "p_body = R_body_from_lidar @ p_lidar + lidar_translation_body_m",
+    "increment": "each row after the first stores the translation of the current LiDAR origin expressed in the previous LiDAR frame and a quaternion mapping current LiDAR vectors into the previous LiDAR frame",
+    "translation_scale": "measured_delta = (lidar_translation_scale + lidar_scale_drift_per_sec * (timestamp_s - first_lidar_timestamp_s)) * true_delta + noise_or_scan_matching_fault",
+    "rotation_scale": "rotation increments are not scaled"
+  },
+  "trajectory_output": "trajectory.csv must estimate body pose in ENU at every timestamp in trajectory_query.csv; quaternion xyzw maps body vectors into ENU"
+}

navigation-sensor-calibration/input/development/survey_pose.csv

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