Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics

Despite recent advances in deep learning, most existing learning IMU odometry methods are trained on specific datasets, lack generalization, and are prone to overfitting, which limits their real-world application. To address these challenges, we present Tartan IMU, a foundation model designed for generalizable, IMU-based state estimation across diverse robotic platforms. Our approach consists of three-stage: First, a pre-trained foundation model leverages over 100 hours of multi-platform data to establish general motion knowledge, achieving 36% improvement in ATE over specialized models. Second, to adapt to previously unseen tasks, we employ the Low-Rank Adaptation (LoRA), allowing positive transfer with only 1.1 M trainable parameters. Finally, to support robotics deployment, we introduce online test-time adaptation, which eliminates the boundary between training and testing, allowing the model to continuously "learn as it operates" at 200 FPS in real-time.