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

Wagih, Hassan; Osman, M.E.A.; Awad, Mohammed I.; Hammad, Sherif

doi:10.1109/ITSC55140.2022.9921796

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

Conference paper (2022)

Authors

Hassan Wagih Ain Shams University

M.E.A. Osman Team Manon Kok - Mechanical, Maritime and Materials Engineering

Mohammed I. Awad Ain Shams University

Sherif Hammad Ain Shams University

Research Group

Team Manon Kok (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/ITSC55140.2022.9921796

Intelligent vehicles Pose estimation Cameras Feedforward neural networks Estimation error Motion estimation Feature detection

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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Manon Kok

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.

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