ros2-bag-data-audit - robot log anomaly report

A structured audit of ROS2 bag exports that measures topic health, detects planted anomalies independently, and separates evidence from candidate causes.

The problem. A bag can record cleanly and still contain operationally bad data. Rate drops, dropout gaps, clock skew, sensor-range violations, command/odometry disagreement and missing transforms all need one evidence-graded report before anyone starts diagnosing the robot.

Result on the committed run: 7 topics audited, 6 of 6 deliberately planted anomalies independently detected, 10 total findings. The scanner never reads the list of planted faults - it finds them or it does not.

Findings are graded, not asserted: 5 confirmed evidence, 1 likely cause, 1 unverified hypothesis, 3 needing additional data. A monitoring tool that states a cause it cannot prove is worse than one that says it does not know yet.

Code and full report: github.com/jigonyoo/ros2-bag-data-audit