IMU Preintegrated Features for Efficient Deep Inertial Odometry
This work addresses the conflict between accuracy and efficiency in deep inertial odometry for low-power and edge applications like pedestrian motion estimation and autonomous vehicles, though it is incremental as it builds on existing learning-based methods.
The paper tackles the high computational and memory demands of deep inertial odometry by proposing IMU preintegrated features as a replacement for raw IMU data, showing performance improvements and reduced computational burdens, including an embedded implementation on a resource-constrained microcontroller.
MEMS Inertial Measurement Units (IMUs) as ubiquitous proprioceptive motion measurement devices are available on various everyday gadgets and robotic platforms. Nevertheless, the direct inference of geometrical transformations or odometry based on these data alone is a challenging task. This is due to the hard-to-model imperfections and high noise characteristics of the sensor, which has motivated research in formulating the system as an end-to-end learning problem, where the motion patterns of the agent are exploited to facilitate better odometry estimates. However, this benefit comes at the cost of high computation and memory requirements, which makes deep inertial odometry unsuitable for low-power and edge applications. This paper attempts to address this conflict by proposing the IMU preintegrated features as a replacement for the raw IMU data in deep inertial odometry. Exploiting the manifold structure of the IMU motion model, these features provide a temporally compressed motion representation that preserves important geometrical information. We demonstrate the effectiveness and efficiency of this approach for the task of inertial odometry on two applications of pedestrian motion estimation and autonomous vehicles. We show a performance improvement compared to raw inputs while reducing the computational burdens. Additionally, we demonstrate the efficiency of this approach through an embedded implementation on a resource-constrained microcontroller.