Sensor Suite Fusion

Problem Statement

Modern UAV autonomy requires simultaneous ingestion of GPS, IMU, lidar, and camera signals. This article documents synchronized sensing pipelines and cross-modality consistency checks.

Fusion Perspective

The stack separates:

• high-rate inertial prediction
• medium-rate positional correction
• environment perception updates
• vision-assisted task-level feedback

A synchronized timeline is essential for stable fusion outputs.

Algorithm Procedure

1. Time-align all sensor streams to a unified clock.
2. Apply per-sensor filtering and outlier rejection.
3. Feed measurements into estimation and mapping modules.
4. Publish fused state and perception artifacts for planning/control.

Tuning and Failure Modes

• Timestamp skew introduces phase lag between modalities.
• Misaligned extrinsic calibration causes map and control bias.
• Overly aggressive filtering can hide real transient dynamics.

Implementation and Execution

python -m uav_sim.simulations.perception.sensor_suite_demo

Evidence

References

• Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems
• Kelly and Sukhatme, Visual-Inertial Sensor Fusion

Related Algorithms

• GPS-IMU Fusion
• Visual Servoing