Analysis of the effect of sensors for end-to-end machine learning odometry

Abstract

Accurate position and orientation estimations are essential for navigation in autonomous robots. Although it is a well studied problem, existing solutions rely on statistical filters, which usually require good parameter initialization or calibration and are computationally expensive. This paper addresses that problem by using an end-to-end machine learning approach. This work explores the incorporation of multiple sources of data (monocular RGB images and inertial data) to overcome the weaknesses of each source independently. Three different odometry approaches are proposed using CNNs and LSTMs and evaluated against the KITTI dataset and compared with other existing approaches. The obtained results show that the performance of the proposed approaches is similar to the state-of-the-art ones, outperforming some of them at a lower computational cost allowing their execution on resource constrained devices.