VANE-IN: Velocity Auto-Encoder for Inertial Navigation

Xiaoqiang Teng, Chenyang Li, Shibiao Xu, Deke Guo, Haisheng Li

Abstract: Data-driven inertial navigation is crucial for mobile computing applications, such as navigation, augmented reality, and robotics. It typically depends on a trained velocity regression network (VRN) to estimate velocities from inertial measurement unit (IMU) data, enabling position determination through integration. However, using prior velocity information for feature representation in inertial navigation remains underexplored. This work introduces a framework called velocity auto-encoder for inertial navigation (VANE-IN), which employs a Teacher-Student scheme to enhance VRN performance by encoding the velocity. Specifically, a velocity auto-encoder (VANE) is proposed as a student model to distill prior velocity insights from the training dataset, which is then guided by the VRN acting as the teacher model. Additionally, an attention mechanism is introduced to fuse these insights into the features of the VRN. To this end, the VANE-IN achieves a state-of-the-art position accuracy on the RoNIN benchmarks. Our experimental results demonstrate that the VANE-IN achieves approximately 5% performance improvements over existing methods regarding position accuracy.

External IDs:dblp:journals/iotj/TengLXGL25