From e0f777837beb34ee2a5e8bb14b1f2c668c412db1 Mon Sep 17 00:00:00 2001 From: Brunsmeier <2970937094@qq.com> Date: Wed, 1 Jul 2026 17:29:58 +0800 Subject: [PATCH] Add linear velocity calculation and visualization to IMU data processing - Implemented Euler to rotation matrix conversion for IMU data. - Added function to compute linear velocity from accelerometer data with gravity compensation. - Enhanced CSV and PNG saving functionality to automatically create directories. - Updated statistics printing to include linear acceleration and velocity metrics. - Expanded plotting functionality to visualize linear acceleration and velocity alongside existing data. - Added new PNG files for IMU quality visualization. --- csv/imu_data.csv | 501 ++++++++++++++++++ csv/imu_data_20260701_170243.csv | 501 ++++++++++++++++++ csv/imu_data_20260701_171025.csv | 500 +++++++++++++++++ csv/imu_data_20260701_171424.csv | 500 +++++++++++++++++ csv/imu_data_20260701_172408.csv | 501 ++++++++++++++++++ png/imu_quality.png | Bin 0 -> 196940 bytes .../imu_quality_20260701_131902.png | Bin png/imu_quality_20260701_170243.png | Bin 0 -> 277343 bytes png/imu_quality_20260701_171025.png | Bin 0 -> 172452 bytes png/imu_quality_20260701_171424.png | Bin 0 -> 178942 bytes png/imu_quality_20260701_172408.png | Bin 0 -> 257307 bytes visualise.py | 208 +++++++- 12 files changed, 2696 insertions(+), 15 deletions(-) create mode 100644 csv/imu_data.csv create mode 100644 csv/imu_data_20260701_170243.csv create mode 100644 csv/imu_data_20260701_171025.csv create mode 100644 csv/imu_data_20260701_171424.csv create mode 100644 csv/imu_data_20260701_172408.csv create mode 100644 png/imu_quality.png rename imu_quality_20260701_131902.png => png/imu_quality_20260701_131902.png (100%) create mode 100644 png/imu_quality_20260701_170243.png create mode 100644 png/imu_quality_20260701_171025.png create mode 100644 png/imu_quality_20260701_171424.png create mode 100644 png/imu_quality_20260701_172408.png diff 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zxu9byF|L4N&yDI4y@J5$)aK zA^N)B>%>F{^2}X@)Z>%7Dp}HF218qJlcJ=g0hnphxCBUR`Ul|ZT9-!B%!X=(3UBj^ zJ*4^$ynrxj6N@~ND3qFMotX^g(eC|K2oHvwde+T0ssyXoC*xjo&=mmxp~SBu<%lYy zXHZs9O@waY;+cC$x27O;Lu(;Du)NT#2}|Fd)k=U2$)F&1ZunS7*a=d!0jf%WzM}vs zN+*4snuswZasd=*?v3rtVgU2S4+s)@lp~dyJUX-K>dFl?0-;Gr-hlotMsSNHA44R7 zlVGbINcM6Po`>jX?Ha8~5iaTHMMOV{huVxdQreRJ6gbJRkuP=Gc@53~ on>Lq!r=Mrvworld rotation matrix. + Assumption: + angle_x = roll + angle_y = pitch + angle_z = yaw + Rotation order: + R = Rz(yaw) @ Ry(pitch) @ Rx(roll) + + 注意：这个顺序要和 IWT603 官方定义确认。 + 如果发现补偿后静止时 linear_acc 不接近 0.需要调整欧拉角顺序或正负号。 + """ + roll = np.deg2rad(roll_deg) + pitch = np.deg2rad(pitch_deg) + yaw = np.deg2rad(yaw_deg) + + cr, sr = np.cos(roll), np.sin(roll) + cp, sp = np.cos(pitch), np.sin(pitch) + cy, sy = np.cos(yaw), np.sin(yaw) + + Rx = np.array([ + [1, 0, 0], + [0, cr, -sr], + [0, sr, cr], + ]) + + Ry = np.array([ + [cp, 0, sp], + [0, 1, 0], + [-sp, 0, cp], + ]) + + Rz = np.array([ + [cy, -sy, 0], + [sy, cy, 0], + [0, 0, 1], + ]) + + return Rz @ Ry @ Rx + + +def add_linear_velocity(samples, gravity_sign=1.0, acc_deadband=0.15, vel_decay=0.995, bias_seconds=1.0): + if not samples: + return samples + + # 先计算每一帧的重力补偿线加速度 + raw_linear_acc_list = [] + + for sample in samples: + acc_body_g = np.array([ + sample["acc_x_g"], + sample["acc_y_g"], + sample["acc_z_g"], + ], dtype=float) + + R = euler_to_rot_matrix( + sample["angle_x_deg"], + sample["angle_y_deg"], + sample["angle_z_deg"], + ) + + acc_world_g = R @ acc_body_g + linear_acc_world_g = acc_world_g - np.array([0.0, 0.0, gravity_sign]) + linear_acc_world = linear_acc_world_g * 9.81 + + raw_linear_acc_list.append(linear_acc_world) + + raw_linear_acc_arr = np.array(raw_linear_acc_list) + + # 用前 bias_seconds 秒估计静止零偏 + t0 = samples[0]["t_s"] + bias_indices = [ + i for i, s in enumerate(samples) + if s["t_s"] - t0 <= bias_seconds + ] + + if bias_indices: + acc_bias = np.mean(raw_linear_acc_arr[bias_indices], axis=0) + else: + acc_bias = np.zeros(3) + + print("Estimated linear acc bias m/s^2:", acc_bias) + + v = np.zeros(3, dtype=float) + last_t = samples[0]["t_s"] + + for i, sample in enumerate(samples): + t = sample["t_s"] + dt = t - last_t + last_t = t + + if dt <= 0 or dt > 0.2: + dt = 0.0 + + linear_acc_world = raw_linear_acc_arr[i] - acc_bias + + if np.linalg.norm(linear_acc_world) < acc_deadband: + linear_acc_world[:] = 0.0 + + v = v * vel_decay + linear_acc_world * dt + + sample["lin_acc_x_ms2"] = linear_acc_world[0] + sample["lin_acc_y_ms2"] = linear_acc_world[1] + sample["lin_acc_z_ms2"] = linear_acc_world[2] + sample["vel_x_ms"] = v[0] + sample["vel_y_ms"] = v[1] + sample["vel_z_ms"] = v[2] + + return samples def unique_path(path): """ - If path already exists, append timestamp before extension. - Example: - imu_quality.png -> imu_quality_20260701_123045.png + Automatically save CSV into ./csv/ + Automatically save PNG into ./png/ + Create folders if they do not exist. """ + if not path: return path - if not os.path.exists(path): - return path + root, ext = os.path.splitext(os.path.basename(path)) + ext = ext.lower() + + if ext == ".csv": + folder = "csv" + elif ext == ".png": + folder = "png" + else: + folder = "output" + + os.makedirs(folder, exist_ok=True) + + new_path = os.path.join(folder, root + ext) + + if not os.path.exists(new_path): + return new_path - root, ext = os.path.splitext(path) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - return "{}_{}{}".format(root, timestamp, ext) + return os.path.join(folder, "{}_{}{}".format(root, timestamp, ext)) def print_stats(samples): if not samples: @@ -238,6 +363,35 @@ def print_stats(samples): float(np.max(freq)), ) ) + if "lin_acc_x_ms2" in samples[0]: + print("\nLINEAR ACC m/s^2") + for field in ("lin_acc_x_ms2", "lin_acc_y_ms2", "lin_acc_z_ms2"): + values = np.array([sample[field] for sample in samples], dtype=float) + diffs = np.diff(values) + diff_std = float(np.std(diffs)) if len(diffs) else 0.0 + print( + " {:16s} mean={: .6f} std={:.6f} ptp={:.6f} diff_std={:.6f}".format( + field, + float(np.mean(values)), + float(np.std(values)), + float(np.ptp(values)), + diff_std, + ) + ) + + if "vel_x_ms" in samples[0]: + print("\nVELOCITY m/s") + for field in ("vel_x_ms", "vel_y_ms", "vel_z_ms"): + values = np.array([sample[field] for sample in samples], dtype=float) + print( + " {:16s} mean={: .6f} std={:.6f} ptp={:.6f} final={: .6f}".format( + field, + float(np.mean(values)), + float(np.std(values)), + float(np.ptp(values)), + float(values[-1]), + ) + ) def plot_samples(samples, output=None, show=True): @@ -245,24 +399,42 @@ def plot_samples(samples, output=None, show=True): raise ValueError("No samples to plot.") t = np.array([sample["t_s"] for sample in samples], dtype=float) - fig, axes = plt.subplots(4, 1, sharex=True, figsize=(13, 9)) + fig, axes = plt.subplots(6, 1, sharex=True, figsize=(13, 13)) plot_group( axes[0], t, samples, ("acc_x_g", "acc_y_g", "acc_z_g"), - "Acceleration (g)", + "Raw Acc (g)", ) + plot_group( axes[1], t, samples, + ("lin_acc_x_ms2", "lin_acc_y_ms2", "lin_acc_z_ms2"), + "Linear Acc (m/s²)", + ) + + plot_group( + axes[2], + t, + samples, + ("vel_x_ms", "vel_y_ms", "vel_z_ms"), + "Velocity (m/s)", + ) + + plot_group( + axes[3], + t, + samples, ("gyro_x_dps", "gyro_y_dps", "gyro_z_dps"), "Gyro (deg/s)", ) + plot_group( - axes[2], + axes[4], t, samples, ("angle_x_deg", "angle_y_deg", "angle_z_deg"), @@ -270,11 +442,11 @@ def plot_samples(samples, output=None, show=True): ) freq = np.array([sample["freq_hz"] for sample in samples], dtype=float) - axes[3].plot(t, freq, label="freq_hz", color="tab:purple", linewidth=1.0) - axes[3].set_ylabel("Hz") - axes[3].set_xlabel("Time (s)") - axes[3].grid(True, alpha=0.3) - axes[3].legend(loc="upper right") + axes[5].plot(t, freq, label="freq_hz", linewidth=1.0) + axes[5].set_ylabel("Hz") + axes[5].set_xlabel("Time (s)") + axes[5].grid(True, alpha=0.3) + axes[5].legend(loc="upper right") fig.suptitle("IMU Data Quality Overview") fig.tight_layout() @@ -328,6 +500,12 @@ def main(): args.max_gap_ms, ) + # 关键：没有样本就直接退出，避免后面 CSV/plot 误报或报错 + if not samples: + print("No samples parsed. Check input mode, serial port, baudrate, or log file format.") + return + + samples = add_linear_velocity(samples, gravity_sign=1.0) print_stats(samples) if args.csv: