Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization

Ma, Shujun; Bai, Xinhui; Wang, Yinglei; Fang, Ruiming

doi:10.3390/s19173747

Cited by 15 publications

(13 citation statements)

References 33 publications

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“…VO scheme could be a good candidate for precise localization in GNSSdenied and textured environments at good illumination conditions. Table 1 [153], [176], [177] and tightly coupled models [158]- [162], [166], [167], [169], [170], [199]. The loosely coupled VIO processes the visual and inertial information independently and each module will estimate camera pose.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…The VIO can be further classified based on type of data fusion into filtering-based [153], [159], [162], [167], [181] and optimization-based [145], [158], [190]- [193] solutions. In general, performing state estimation using Filtering-based VIO is processed in two stages, (i) estimate the vehicle pose using the IMU linear acceleration and angular velocities that drive the vehicle dynamic model and (ii) update the vehicle pose using the key information of the visual data that estimated the vehicle ego-motion.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

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State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

2021

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Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

2021

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“…Feature detection is an abstraction of image information and a local decision-making method for each pixel whether there is a given type of feature. It is a fundamental problem in computer vision and has many practical applications, such as object detection [ 1 ], stereo matching [ 2 ], color matching [ 3 ], and motion estimation [ 4 ]. In order to response to diverse applications, many detection methods have been proposed [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Wang

Zeng

et al. 2020

Sensors

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Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.

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“…In recent years, simultaneous localization and mapping (SLAM) [ 1 ] has become an attractive research topic in many self-localization robotic areas, particularly with the development of mobile robots. The SLAM technology can estimate a robot’s own motion and attitude using sensors without any prior information of the environment.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Nonlinear Optimization Motion Estimation Based on RGB-D and Inertial Fusion

Zhao

Miao

Zhang

2020

Sensors

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To achieve a high precision estimation of indoor robot motion, a tightly coupled RGB-D visual-inertial SLAM system is proposed herein based on multiple features. Most of the traditional visual SLAM methods only rely on points for feature matching and they often underperform in low textured scenes. Besides point features, line segments can also provide geometrical structure information of the environment. This paper utilized both points and lines in low-textured scenes to increase the robustness of RGB-D SLAM system. In addition, we implemented a fast initialization process based on the RGB-D camera to improve the real-time performance of the proposed system and designed a new backend nonlinear optimization framework. By minimizing the cost function formed by the pre-integrated IMU residuals and re-projection errors of points and lines in sliding windows, the state vector is optimized. The experiments evaluated on public datasets show that our system achieves higher accuracy and robustness on trajectories and in pose estimation compared with several state-of-the-art visual SLAM systems.

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Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization

Cited by 15 publications

References 33 publications

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Multi-Feature Nonlinear Optimization Motion Estimation Based on RGB-D and Inertial Fusion

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