Timing that control engineers can trust
Effective rate, interval stability, timestamp jitter, missing periods, duplicates, non-monotonic stamps, cumulative clock drift, bursts, and sequence gaps.
Engineering diagnostic bench
Understand your motion data before trusting it.
Analyze timing, jitter, noise, drift, vibration, Allan deviation, clipping, temperature behavior, calibration readiness, and multi-IMU consistency—directly in your browser.
No account required
Professional report export
Raw data never uploaded
Start a characterizationLocal · private
Drop a recording here
Upload one sensor, several redundant IMUs, or open the included engineering example.
CSV included
MCAP Zstd + LZ4
ROS 1/2 bag + folder
High-rate awareStreaming parsing, bounded analysis buffers, decimated plots.
Technically honestQuality and stability are separated from ground-truth accuracy.
A virtual sensor laboratory, in one page.
From first-pass integrity checks to timing diagnostics, vibration analysis, stochastic noise characterization, temperature dependence, calibration output, and multi-sensor comparison.
No dataset loadedUpload or open example
—Duration
—Samples
—Effective rate
—Data integrity
Ready for a recording
Use your own CSV or open the example dataset to explore the complete analysis workspace.
Motion overview
Acceleration and angular-rate magnitude over time
Acceleration magnitudeAngular-rate magnitude
Main findings
Prioritized engineering observations
Recording summary
Observed properties and acquisition health
Automatic motion segmentation
Stationary and dynamic regions detected from the data
Mean sample rate—From complete recording duration
RMS jitter—Relative to nominal period
Dropped samples—Timestamp + sequence estimate
Clock drift—End-to-end cumulative drift
Sample interval over time
Observed Δt versus nominal period
Jitter distribution
Histogram of interval error
Timing diagnostics
Percentiles, anomalies, and timestamp quality
Interpretation
What the timing trace means for a control system
Header vs storage timestamps
Median-centered transport timing; not assumed to be one-way latency
Clock-domain diagnostics
Offset, spread, buffering, and timestamp selection
Stationary noise, bias, and distribution statistics
Metrics are flagged when motion contaminates a stationary estimate
| Channel | Mean / bias | Std. deviation | RMS | Peak-to-peak | Skewness | Kurtosis |
|---|
Channel distribution
Robust histogram around the median
Gravity sanity check
Useful for stationary scale and calibration checks
Mean measured gravity—
Deviation from standard gravity—
Gravity magnitude noise—
Rolling bias and noise
Windowed mean and standard deviation through the recording
Autocorrelation
Normalized correlation versus lag for the selected channel
Six-axis correlation matrix
Pearson correlation on the qualified stationary subset
Frequency & vibration lab
Welch power spectral density with dominant peak and alias-risk detection
Power spectral density
One-sided PSD, Hann window, overlapping Welch averages
Dominant components
Ranked non-DC spectral peaks
| Frequency | Relative level | Assessment |
|---|
Cumulative spectral RMS
Integrated RMS contribution from DC to frequency
Time-frequency spectrogram
Windowed vibration energy over elapsed time
Allan deviation & stochastic noise
Overlapping Allan deviation with dataset-sufficiency checks
Allan deviation
Logarithmic averaging time and deviation
Random walk estimate
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—
Bias instability estimate
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Temperature-model residuals
Observed channel minus fitted thermal response
Warm-up response
Temperature and selected-axis drift versus elapsed time
Bias versus temperature
Linear dependency preview; polynomial models belong in a dedicated temperature sweep
Temperature model
Observed dependency in this recording
Temperature span—
Linear slope—
Coefficient of determination—
Zero-rate gyroscope estimate
Mean vector during detected stationary samples
Accelerometer sanity estimate
Gravity magnitude and apparent scale residual
Guided six-position calibration
Upload labeled +X, −X, +Y, −Y, +Z, −Z stationary captures for full bias, scale, misalignment, and non-orthogonality estimation
+X upWaiting for labeled capture
−X upWaiting for labeled capture
+Y upWaiting for labeled capture
−Y upWaiting for labeled capture
+Z upWaiting for labeled capture
−Z upWaiting for labeled capture
Multi-IMU comparison
Compare two or three recordings after unit normalization and common-frame preparation
| Recording | Rate | RMS jitter | Gyro RMS | Accel RMS | Dropped | Assessment |
|---|
Comparison interpretation
Potential outliers and shared failure modes
Fusion simulation
Mean, weighted, median, residual rejection, and health-based exclusion
The interface is prepared for synchronized, common-frame recordings. A production deployment should estimate time offsets, apply mounting rotations, resample, and calculate pairwise residuals before comparing fusion methods.
Olive Robotics · Controlled engineering document
IMU Full Characterization Report
A traceable engineering assessment of timing, integrity, transport behavior, six-axis statistics, noise, vibration, stochastic stability, temperature response, and calibration readiness.
Professional report center
Direct downloads with provenance, methods, validity gates, units, settings, and limitations
Report qualification
Availability and confidence of evidence sections
Measurement boundary: the reports characterize evidence present in the recording. Timestamp gaps are not called packet loss without sequence evidence; header-to-storage differences are not called one-way latency without known clock semantics; and absolute motion or orientation accuracy requires synchronized traceable ground truth.
Noise and long-term stability
Bias, RMS noise, robust distributions, gravity checks, Allan deviation, random walk, bias-instability estimates, and reliability warnings when data is insufficient.
Frequency and vibration insight
FFT/Welch PSD, dominant peaks, harmonics, motor-RPM correlation, resonance clues, aliasing risk, and axis-dependent vibration behavior.
Temperature behavior
Bias versus temperature, warm-up behavior, regression quality, compensation-model preparation, and explicit warnings when normal motion contaminates thermal estimates.
Calibration-ready output
Zero-rate bias estimates, gravity scaling checks, six-position workflow scaffolding, and exports designed for ROS 2 YAML or embedded firmware integration.
Multi-redundant IMU comparison
Normalize recordings, compare bias/noise/timing, expose outliers, and prepare for timestamp alignment, mounting transforms, residuals, fault isolation, and robust fusion studies.
Your sensor data stays on your machine.
CSV processing, statistics, spectral calculations, visualizations, and report generation run inside the browser. No raw recording is sent to Olive Robotics or a third-party server by this component.
Confirm data mapping
Review detected fields, units, sensor range, and test context.
Timestamp and optional sequence
Accelerometer channels
Gyroscope channels
Metadata and analysis context
Range and custom scaling
Why this matters: data filtered to 20 Hz should not be compared directly with data at 400 Hz bandwidth, and dynamic robot motion should not be reported as stationary sensor noise. The report records this context.
Analysis begins locally after you confirm this mapping.
Ready.
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Embedded Physical AI for Robots.