ROVINS: Robust Omnidirectional Visual Inertial Navigation System

Seok, Hochang; Lim, Jongwoo

doi:10.1109/lra.2020.3010457

Cited by 29 publications

(5 citation statements)

References 28 publications

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“…The pose is estimated with a proposed multi-view P3P RANSAC-based algorithm and the online camera calibration is proposed in optimization to overcome the deformation and motion of the camera. They further extended their work to omnidirectional visual-inertial navigation to overcome the shortcomings that affect visual sensors, such as fast motion and sudden illumination changes [29]. The soft relative pose constraints from the inertial sensor are added to pose optimization to deal with blind motion estimation, and the visual features in tracking are initialized based on estimated velocity from prediction results.…”

Section: Related Workmentioning

confidence: 99%

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

Javed

Kim

2022

IEEE Access

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With the recent developments in computer vision, vision-based odometry plays an increasingly important role in the field of autonomous systems. However, using traditional visual odometry or simultaneous localization and mapping (vSLAM) only performs better in simple environments with obvious structural features. Visual odometry may easily fail in a complex environment due to the sparsity of stable features, sensor failure, and extreme weather conditions or sunlight problems. Monocular camera-based traditional algorithms are highly affected by these issues, leading to stability and reliability problems. Therefore, to deal with such issues, a vision-based Omni-directional odometry based on multi-camera collaboration is proposed. The development of feature-based omnidirectional odometry and feature prioritization to limit the computational complexity offered by multiple cameras is the major contribution. Firstly, the multicamera state perception model is developed based on the spherical camera, which guarantees an accurate transformation from the camera to the spherical coordinate system. The feature detection and tracking are performed in the images of the individual cameras in a parallel thread. The features tracking across the different cameras prevent failures, however, it also adds extra computational complexity for the pose estimation and optimization module. To budget the feature distribution, a feature prioritization algorithm is proposed to limit the number of features. Furthermore, the multi-view pose refinement module further reduces the complexity of the system. The feature prioritization helps to maintain the smaller set of tracked features with similar accuracy and less computational complexity. Finally, the pose is estimated in a spherical coordinate system by projecting all the successfully tracked key points to the sphere with the help of the omnidirectional perception model. To validate the proposed method, the data set is collected from the outdoor environment with ground truth provided by high-accuracy GPS. Detailed qualitative and quantitative evaluations are performed, which show that the proposed algorithm improves the position accuracy by about 40%-60% as compared with state-of-the-art methods with limited computation time.INDEX TERMS panoramic camera, multi-camera, visual odometry, vSLAM, GNSS/INS, Monocular camera.

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Section: Related Workmentioning

confidence: 99%

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

Javed

Kim

2022

IEEE Access

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“…A unique omnidirectional setup was proposed by Seok et al [5] with four very large FoV cameras. The four overlapping image regions were treated as four stereo cameras, but the system did not fully take advantage of the camera setup to track features across the camera pairs.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, if the cameras are arranged to have overlapping Fields of View (FoV), a particular feature could potentially be tracked across the cameras for as long as it is visible in any camera. Multicamera solutions have been presented in the past [4], [5], [6], however, no proposal has studied cross camera feature tracking.…”

Section: Introductionmentioning

confidence: 99%

Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry

Zhang,

Wisth,

Camurri

et al. 2021

Preprint

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We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments such as in narrow corridors and dark spaces with aggressive motions and abrupt lighting changes. These scenarios cause traditional monocular or stereo odometry to fail. While tracking motion across extra cameras should theoretically prevent failures, it causes additional complexity and computational burden. To overcome these challenges, we introduce two novel methods to improve multi-camera feature tracking. First, instead of tracking features separately in each camera, we track features continuously as they move from one camera to another. This increases accuracy and achieves a more compact factor graph representation. Second, we select a fixed budget of tracked features which are spread across the cameras to ensure that the limited computational budget is never exceeded. We have found that using a smaller set of informative features can maintain the same tracking accuracy while reducing backend optimization time. Our proposed method was extensively tested using a hardware-synchronized device containing an IMU and four cameras (a front stereo pair and two lateral) in scenarios including an underground mine, large open spaces, and building interiors with narrow stairs and corridors. Compared to stereoonly state-of-the-art VIO methods, our approach reduces the drift rate (RPE) by up to 80 % in translation and 39 % in rotation.

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“…Vision-based inertial odometry (VIO) is a technique that combines cameras and inertial measurement units (IMUs) to enhance motion tracking performance [1,2]. Cameras makes it adaptable to various complex environments and scenarios.…”

Section: Introductionmentioning

confidence: 99%

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Fu,

Shi,

et al. 2024

Meas. Sci. Technol.

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Traditional vision-based inertial odometry suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units. First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

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ROVINS: Robust Omnidirectional Visual Inertial Navigation System

Cited by 29 publications

References 28 publications

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

OmniVO: Toward Robust Omni Directional Visual Odometry With Multicamera Collaboration for Challenging Conditions

Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

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