End-to-End Learning Framework for IMU-Based 6-DOF Odometry.

ABSTRACT: This paper presents an end-to-end learning framework for performing 6-DOF odometry by using only inertial data obtained from a low-cost IMU. The proposed inertial odometry method allows leveraging inertial sensors that are widely available on mobile platforms for estimating their 3D trajectories. For this purpose, neural networks based on convolutional layers combined with a two-layer stacked bidirectional LSTM are explored from the following three aspects. First, two 6-DOF relative pose representations are investigated: one based on a vector in the spherical coordinate system, and the other based on both a translation vector and an unit quaternion. Second, the loss function in the network is designed with the combination of several 6-DOF pose distance metrics: mean squared error, translat

SUBMITTER: Silva do Monte Lima JP 

PROVIDER: S-EPMC6749526 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature