Sensor Fusion

01

Fusion Combines Complementary Evidence

Source-backed context[1] ROS robot_localization state-estimation documentation[2] EuRoC visual-inertial dataset and calibration

Sensors have different strengths and failure modes. Cameras provide rich appearance but can struggle with blur, glare, darkness, or low texture. IMUs update quickly but drift when integrated over time. GNSS provides global position outdoors but can fail indoors. Force sensing reveals contact that may be visually occluded.

Fusion uses redundancy and complementarity to estimate a quantity such as pose, velocity, object state, or contact. It is not simply placing arrays beside one another. The algorithm must understand when and where each measurement applies and how uncertain it is.

02

Timing and Calibration Come First

Source-backed context[2] EuRoC visual-inertial dataset and calibration[3] Project Aria multimodal data formats

Measurements from different clocks cannot be compared safely until their time relationship is known. Offset, drift, latency, rolling shutter, exposure time, and sensor buffering can all affect alignment. High-rate signals may need interpolation or windowing around lower-rate camera frames.

Spatial fusion also requires coordinate transforms. Camera intrinsics describe image geometry. Extrinsics describe how sensors are positioned relative to each other or the robot. Frame names, axis directions, handedness, calibration method, and calibration version must remain with the data.

03

Uncertainty and Validity Are Part of the Input

Source-backed context[1] ROS robot_localization state-estimation documentation[4] ROS 2 IMU message and covariance fields

Fusion systems decide how much to trust each measurement. Covariance, confidence, sensor health, saturation, dropout, and environmental conditions can change that trust. If a dataset exports only nominal values, the learner or estimator cannot distinguish a reliable signal from one captured during failure.

Missingness should be explicit. Filling unavailable values with zero creates false measurements. Valid masks, timestamps, error codes, quality flags, and raw sensor records allow downstream systems to reject, repair, or model uncertainty deliberately.

04

Sensor Fusion Data Failure Modes

Source-backed context[1] ROS robot_localization state-estimation documentation[2] EuRoC visual-inertial dataset and calibration[3] Project Aria multimodal data formats

A fusion dataset can look synchronized at ordinary motion and fail during rapid rotation, impact, or contact. Stale calibration, unmeasured clock drift, unknown filtering, and inconsistent covariance are common causes. Training only on healthy signals also leaves the system unprepared for degraded sensors.

Pilot acceptance should inspect difficult time windows, compare independent measurements, replay calibration, quantify gaps, and verify that all transforms and validity fields reach the intended estimator or model. Source signals should remain available even when a fused output is delivered.

• Shared timestamps without a proven shared timebase
• Stale or session-mismatched extrinsic calibration
• Filtered signals delivered without filter metadata
• Covariance absent, constant, or semantically incorrect
• Sensor dropouts hidden by interpolation

05

What a Buyer Should Specify

Implementation guidanceEGXO guidance for translating the definition into a project specification.

Specify the fused state or learning target first, then the required source signals. Define rates, timing, calibration, frames, uncertainty, raw retention, and degraded-sensor scenarios. The data package should support independent reproduction of the fusion input pipeline.

• Target estimate or model input produced by fusion
• Required sensors, rates, placements, and operating ranges
• Clock synchronization, offset, drift, and latency evidence
• Intrinsics, extrinsics, coordinate frames, and calibration cadence
• Uncertainty, validity, saturation, and dropout representation
• Raw source streams and reproducible preprocessing lineage