A Review of Visual Odometry Methods and Its Applications for Autonomous Driving
The research into autonomous driving applications has observed an increase in computer vision-based approaches in recent years. In attempts to develop exclusive vision-based systems, visual odometry is often considered as a key element to achieve motion estimation and self-localisation, in place of wheel odometry or inertial measurements. This paper presents a recent review to methods that are pertinent to visual odometry with an emphasis on autonomous driving. This review covers visual odometry in their monocular, stereoscopic and visual-inertial form, individually presenting them with analyses related to their applications. Discussions are drawn to outline the problems faced in the current state of research, and to summarise the works reviewed. This paper concludes with future work suggestions to aid prospective developments in visual odometry.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingMotion EstimationVisual OdometrySimilar Papers 제목 키워드 기반
Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods
This paper fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the bi…
Autonomous DrivingVisual OdometryCodedVO: Coded Visual Odometry
Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we present…
Monocular Visual OdometryVisual OdometryVision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss th…
Optical Flow EstimationSimultaneous Localization and MappingVisual LocalizationVisual OdometryVSO: Visual Semantic Odometry
Robust data association is a core problem of visual odometry, where image-to-image correspondences provide constraints for camera pose and map estimation. Current state-of-the-art direct and indirect methods use short-te…
Autonomous DrivingVisual OdometryA 2.5D Vehicle Odometry Estimation for Vision Applications
This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems. Based on a set of commonly deployed vehicular …
Autonomous Driving