Autonomous flight with robust visual odometry under dynamic lighting conditions

Kim, Pyojin; Lee, Hyeonbeom; Kim, H. Jin

doi:10.1007/s10514-018-9816-4

Cited by 17 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…State-of-the-art solutions for self-localization in lowvisibility environments can be categorized as follows: single modality or multi-modality frameworks. In a single modality, the state estimation was performed based on data obtained from a single sensor i.e., stereo-based VO [218], thermalbased VO [106], or event-based [85]. Thermal sensors provide images at low resolution with low textured and features.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…To enhance robustness to difficult illumination conditions and high dynamic range (HDR) environments, enhanced VO frameworks are proposed in the literature, such as by using binary descriptors [216], affine illumination model [218], and learning-based methods to enhance image representation [215]. Moreover, a multi-modality framework has proposed to cope with difficulties in percepting the environment around the vehicle at low visibility.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…Citation information: DOI 10.1109/ACCESS.2021.3082778, IEEE Access Yusra Alkendi et al: State of the Art in Vision-based Localization Techniques for Autonomous Navigation SystemsPark et al[217] have performed a systematic evaluation of the performance of various direct image alignment methods in terms of accuracy and robustness under significant illumination changes. Kim et al[218] have proposed a stereo VO algorithm which employs affine illumination model in each image patch to cope with abrupt illumination variation in direct state estimation model. Vidal et al[220] have proposed a hybrid framework that fuses event data, visual data from standard images, and IMU measurements for a more accurate and robust pose estimation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

2021

View full text Add to dashboard Cite

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.

show abstract

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

2021

View full text Add to dashboard Cite

show abstract

“…However, its front-end and back-end are almost independent, which cannot share estimation information to improve the poses precision further. In [27], a semi-direct approach was proposed for stereo odometry. This method uses the feature-based method to obtain a motion estimation, and then perform direct semi-dense to refine the camera pose.…”

Section: Related Workmentioning

confidence: 99%

Semi-Direct Monocular SLAM With Three Levels of Parallel Optimizations

Zhi

Zhang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In practical applications, how to use the complementary strengths of the direct and the feature-based methods for effective fusion may be the main challenge of simultaneous localization and mapping (SLAM). To solve this challenge, we propose the DO-SLAM, a novel fast and accurate semidirect visual SLAM framework, which can maintain the direct method's fast performance and the high precision and loop closure capability of the feature-based method. The direct method is used as the first half of the DO-SLAM to track the camera pose rapidly and robustly. The feature-based method is used as the second half of the DO-SLAM to refine the keyframe poses, perform loop closures, and build a globally consistent, long-term, sparse feature map that can be reused. The proposed pipeline fuses direct odometry and feature-based SLAM to perform three levels of parallel optimizations: (1) In the direct method module, the keyframe poses are estimated by minimizing the photometric error, (2) In the feature-based module, using the poses calculated by the inter-frame matching to correct and fuse the poses calculated by the direct method module as the initial poses, and the initial poses are optimized by the motion-only bundle adjustment, and (3) A pose graph optimization is used to achieve global map consistency in the presence of loop closures. Experimental evaluation on two benchmark datasets demonstrates that the proposed approach achieves higher accuracy and robustness on motion estimation compared to the other state-of-the-art methods.INDEX TERMS simultaneous localization and mapping(SLAM), semi-direct SLAM, three levels of parallel optimizations

show abstract

“…However, its frontend and backend are almost independent, which cannot share estimation information to further improve the pose precision. In [25,26], different semi-direct approaches were proposed for stereo odometry. Both methods use feature-based tracking to obtain a motion prior, and then perform direct semi-dense or sparse alignment to refine the camera pose.…”

Section: Introductionmentioning

confidence: 99%

SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

Liu

Wang

2020

Sensors

View full text Add to dashboard Cite

In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.

show abstract

Autonomous flight with robust visual odometry under dynamic lighting conditions

Cited by 17 publications

References 12 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

Semi-Direct Monocular SLAM With Three Levels of Parallel Optimizations

SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

Contact Info

Product

Resources

About