Self-Supervised Inertial Odometry

Key points

• Learning inertial odometry for a new platform usually requires ground-truth trajectories.1
• Pose graph distillation then refines the model with sequence-consistent targets from a pose graph over many windows.1
• Training on a new platform therefore no longer requires collecting ground-truth trajectories, but only calibrating the IMU and recording data during normal operation.1
• Across six datasets spanning pedestrians, drones, quadrupeds, and a humanoid, the same recipe with no velocity or position labels trains models that come within $0.07$ m/s average velocity error of a same-architecture supervised baseline.1

Sources (1)

• [1]Self-Supervised Inertial Odometry
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:14 AM

  We show that an inertial odometry model can instead be trained from scratch by a myopic teacher that integrates the IMU over each short window independently, a scheme we call integration self-supervision.Interestingly, although supervised only by short-window velocity changes, the student learns to directly predict velocity, and does so more accurately than its integration teacher.

  Learning inertial odometry for a new platform usually requires ground-truth trajectories.

Extractive summary: sentences quoted from the sources.