Informational Framework for Minimalistic Visual Odometry on Outdoor Robot

Hage, Joelle Al; Mafrica, Stefano; Najjar, Maan El Badaoui El; Ruffier, Franck

doi:10.1109/tim.2018.2871228

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…However, GPS/GNSS positioning and angle measurement inaccuracies led to mismatching of point clouds collected from different positions and angles. Angle measurement is based on the mathematical integration of IMU sensor outputs, and accumulated errors can cause drift in angle measurement results (Al Hage et al 2019). Boom vibrations and point cloud density may also affect volume measurement accuracy.…”

Section: Discussionmentioning

confidence: 99%

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Niskanen

Immonen

Hallman

et al. 2023

J. Transp. Eng., Part B: Pavements

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Construction materials and related management, handling, and storage provisions account for a large part of road construction expenses. For that reason, improved material flow monitoring techniques can achieve significant cost and time savings, as well as better quality control. The study assessed the performance of a solid-state pulsed time-of-flight laser lidar profilometer in measuring the volume of soil stockpiles and road layer thicknesses. The 3D (X, Y, Z, and intensity) image calculation was based on the analysis of multiple combined point clouds measured with an excavator-integrated profilometer. Error analysis confirmed the accuracy of road layer thickness estimation within one centimetre and an error level of approximately 3% when measuring soil stockpile volumes. In combination with a theoretical model of the superstructure, this 3D measurement technique can help contractors and supervisors to ensure road quality.

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Section: Discussionmentioning

confidence: 99%

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Niskanen

Immonen

Hallman

et al. 2023

J. Transp. Eng., Part B: Pavements

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“…In recent progress, several VIO techniques have performed robust estimations from multi-sensor data fusion [ 19 , 20 ]. However, the robustness issues of monocular vision methods still require further investigation.…”

Section: Introductionmentioning

confidence: 99%

InertialNet: Inertial Measurement Learning for Simultaneous Localization and Mapping

Lin,

Liu,

Lin

2023

Sensors

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SLAM (simultaneous localization and mapping) plays a crucial role in autonomous robot navigation. A challenging aspect of visual SLAM systems is determining the 3D camera orientation of the motion trajectory. In this paper, we introduce an end-to-end network structure, InertialNet, which establishes the correlation between the image sequence and the IMU signals. Our network model is built upon inertial measurement learning and is employed to predict the camera’s general motion pose. By incorporating an optical flow substructure, InertialNet is independent of the appearance of training sets and can be adapted to new environments. It maintains stable predictions even in the presence of image blur, changes in illumination, and low-texture scenes. In our experiments, we evaluated InertialNet on the public EuRoC dataset and our dataset, demonstrating its feasibility with faster training convergence and fewer model parameters for inertial measurement prediction.

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“…Visual odometry (VO) estimates the camera motions from the sequence of images. VO has also become a popular navigation topic in recent years, and scholars have done a lot of research in this area [12][13][14]. But VO also has some shortcomings; for example, it does not know when the camera has moved or whether the external environment has changed when the image changes.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive federated filter for GNSS/INS/VO integrated navigation system

Yue

Lian

Tang

et al. 2020

Meas. Sci. Technol.

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In order to solve the problem of decreased navigation performance of the Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation systems in GNSS-denied environments, and to improve the navigation accuracy and robustness of the navigation system, a novel adaptive federated filter with a feedback scheme for a GNSS/INS/visual odometry (VO) integrated navigation system is proposed in this paper. A visual-inertial odometry system model with a multi-state constraint Kalman filter structure based on a polar geometry and trifocal tensor geometry between different images is established, which can provide better navigation accuracy in GNSS-denied environments. Moreover, a new method to obtain the information allocation factor according to the different navigation performances of local filters is deduced in this paper, which has low computational complexity and a simple structure. Meanwhile, an abnormal measurement detection algorithm based on fuzzy logic is proposed to detect the abnormal measurements of local filters. The results of the vehicle experiment with the publicly available real-world KITTI dataset show that the proposed algorithm can obtain reliable navigation results in GNSS-denied environments and improve the navigation accuracy and robustness of the GNSS/INS/VO integrated navigation system.

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Informational Framework for Minimalistic Visual Odometry on Outdoor Robot

Cited by 16 publications

References 19 publications

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

Using a 2D Profilometer to Determine Volume and Thickness of Stockpiles and Ground Layers of Roads

InertialNet: Inertial Measurement Learning for Simultaneous Localization and Mapping

A novel adaptive federated filter for GNSS/INS/VO integrated navigation system

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