Stereo Visual Odometry Based on Motion Decoupling and Special Feature Screening for Navigation of Autonomous Vehicles

Ci, Wenyan; Hu, Xing

doi:10.1109/jsen.2019.2917936

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…In mobile robotic systems, the autonomous vehicle is a rapidly advancing application area of visual odometry with unstructured environments [109]- [111]. Visual odometry is generally regarded as a key for truly autonomous vehicles.…”

Section: Application Of Visual Odometry In Complicated and Emerging A...mentioning

confidence: 99%

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Wang,

Ma,

Chen

et al. 2020

Preprint

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Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometrybased methods, deep learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep learning-based VO (Deep VO). Therefore, this paper aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented reality and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.

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Section: Application Of Visual Odometry In Complicated and Emerging A...mentioning

confidence: 99%

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Wang,

Ma,

Chen

et al. 2020

Preprint

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“…Consequently, extensive research has been conducted to develop various positioning systems and techniques for mobile robots [2] and self-driving cars [3] using sensors [4]- [6] such as wheel odometry, ultrasonic odometry, GNSS, inertial navigation system (INS), and visual odometry (VO). It is also crucial to note that these methods are not restrictive, as odometry methods can be multi-modal, i.e., sensor fusion of different sensors can be combined in a single algorithm [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Body-Centered Dynamically-Tuned Error-State Extended Kalman Filter for Visual Inertial Odometry in GNSS-Denied Environments

Abdelaziz,

Elghamrawy,

Noureldin

et al. 2024

IEEE Access

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The use of visual odometry in autonomous land vehicle and unmanned ground vehicle (UGV) applications has shown to be promising in providing robust and accurate positioning and navigation in GNSS-denied environments. While GNSS is the most accurate and reliable positioning source, it cannot be received indoors. Furthermore, low-cost micro-electro-mechanical system (MEMS)-based inertial sensors are used for positioning but can result in rapid drift during prolonged GNSS outages. To address these limitations, this paper proposes an innovative method of integrating MEMS-based inertial sensors with a vision-based navigation system. The vision-based navigation relies on a visual odometry pipeline modified to replace the feature-matching step with Lucas-Kanade optical flow. While the inertial-based system is derived from the three-dimensional reduced inertial sensor system (3D-RISS) and modified for a bodycentered update. The fusion of both systems is based on an Extended Kalman filter (EKF) to estimate the position and heading information and correct for inertial sensor errors. To assess the effectiveness of the proposed method, real indoor and road test trajectories were conducted using both a Husky A200 UGV and a sensor-equipped minivan. The results show that the body-centered inertial system, aided with reliable vision updates, provides a reliable positioning solution for an extended duration in GNSS-denied environments.

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“…In the most fundamental manifestation the vehicle motion is derived by the integration of wheel motion relying on parameters such as wheel radius and baseline [2]. This results in ego motion estimation (dead reckoning) which is needed for autonomous navigation [3], mapping [4], [5] and obstacle avoidance [6], [7] for intelligent vehicles [8]. In addition, the mobile robotic community proposes methodologies for motion estimation based on probability theory known as probabilistic robotics [9].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Calibration via Linear Optimization

Novotny¹,

Liu²,

Wöber³

et al. 2022

Preprint

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In navigation activities, kinematic parameters of a mobile vehicle play a significant role. Odometry is most commonly used for dead reckoning. However, the unrestricted accumulation of errors is a disadvantage using this method. As a result, it is necessary to calibrate odometry parameters to minimize the error accumulation. This paper presents a pipeline based on sequential least square programming to minimize the relative position displacement of an arbitrary landmark in consecutive time steps of a kinematic vehicle model by calibrating the parameters of applied model. Results showed that the developed pipeline produced accurate results with small datasets.

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Stereo Visual Odometry Based on Motion Decoupling and Special Feature Screening for Navigation of Autonomous Vehicles

Cited by 18 publications

References 20 publications

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Body-Centered Dynamically-Tuned Error-State Extended Kalman Filter for Visual Inertial Odometry in GNSS-Denied Environments

Autonomous Vehicle Calibration via Linear Optimization

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