Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks

TL;DR

This paper introduces a neural network-based method to reduce drift in monocular visual odometry for intelligent vehicles, improving pose estimation accuracy by correcting propagated errors.

Contribution

It presents a novel neural network approach trained on KITTI data to specifically reduce drift in monocular visual odometry, enhancing vehicle pose accuracy.

Findings

Significant reduction in orientation estimation errors.

Improved overall pose accuracy in KITTI dataset tests.

Effective correction of feature detection and matching errors.

Abstract

In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural network reduces the errors in the pose estimation of the vehicle which results from the inaccuracies in features detection and matching, camera intrinsic parameters, and so on. These inaccuracies are propagated to the motion estimation of the vehicle causing larger amounts of estimation errors. The drift reducing neural network identifies such errors based on the motion of features in the successive camera frames leading to more accurate incremental motion estimates. The proposed drift reducing neural network is trained and validated using the KITTI dataset and the results show the efficacy of the proposed approach in reducing the errors in the incremental orientation estimation, thus reducing the overall error in the pose estimation.