When Does Learned Inertial Odometry Fail?

Learned inertial odometry estimates velocity directly from IMU sequences and reduces the drift caused by inertial integration, but its cross-domain generalization remains limited. Existing studies mainly focus on improving target-domain accuracy, while aggregate metrics provide little insight into why failures occur. We analyze target-domain failures by relating them to source-domain motion coverage, source prediction error, and model internal states. Our results show that source motion coverage alone does not determine prediction failure. Some failures occur under motion conditions that are rare in the source domain, while others arise under source-covered conditions near source low-error motion regions. For the latter, failed predictions tend to remain biased toward the source-domain velocity associated with similar motion. This indicates that motion–velocity relations learned from source data may become invalid after domain shift. We further show that different failure types favor different target-domain adaptation data, and that motion and internal-state information provide complementary cues for failure detection. These results link the data regularities that enable direct velocity prediction to the cross-domain failures of learned inertial odometry.