An improved SLAM based on RK-VIF: Vision and inertial information fusion via Runge-Kutta method

Cui, Jiashan; Zhang, Fangrui; Feng, Dongzhu; Li, Cong; Li, Fēi; Tian, Qichen

doi:10.1016/j.dt.2021.10.009

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…[21] When used for image matching error rejection, the efficiency is more than double that of RANSAC, and it has good robustness. Ma Xiaomin et al [22] proposed a method to construct a nonlinear scale space with complete affine invariance by combining asymptotic sampling optimization and nonlinear diffusion filtering, and improved the matching speed and accuracy through vector field consistency (VFC) and progressive sampling consistency (PROSAC). [23~24] The construction of a nonlinear scale space using classical nonlinear diffusion filtering to be matched images is usually implemented by the nonlinear partial differential P-M diffusion equation:…”

Section: Asymptotic Consistent Samplingmentioning

confidence: 99%

Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics

He,

Yu,

2024

Preprint

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Feature matching is an essential part in areas such as target tracking, and three-dimensional reconstruction. In case of rotational motion in the image, the rotating exercise core 8 statistical motion support volume is applied, resulting in low matching accuracy and long time to eliminate mismatching. A principal component analysis method is proposed to calculate rotation angle, feature points are changed in the grid and its neighborhood grid, which sets Gaussian threshold according to Euclid distance between neighborhood feature point and the matching feature point. And a new fractional statistical model is proposed to increase the number of correct matching pairs, So as to improve the fastness and accuracy of characteristic matching. Aiming at the problem of mismatch caused by local similarity of images, a data set is proposed to determine the data set by using geometric relationship between feature points, which analyzes the similarity between the data by the Person correlation coefficient, and sets the threshold to remove the feature matching pairs with low confidence, so as to improve the accuracy of feature matching. Experimental results show that the feature matching speed of the improved GMS algorithm is 3 times that of original GMS algorithm, and the false matching is eliminated in local similar region, which improves the quality of feature matching.

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Section: Asymptotic Consistent Samplingmentioning

confidence: 99%

Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics

He,

Yu,

2024

Preprint

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“…By leveraging the ego-car speed, we can effectively filter out noise points, thereby significantly enhancing the accuracy of range flow estimation. In this study, we employed the fourth order Runge-Kutta integration to discretize the IMU motion formula [27], allowing us to obtain IMU odometry and velocity.…”

Section: Imu Measurementsmentioning

confidence: 99%

Removing Moving Objects without Registration from 3D LiDAR Data Using Range Flow Coupled with IMU Measurements

Cai

Zhou

et al. 2023

Remote Sensing

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Removing moving objects from 3D LiDAR data plays a crucial role in advancing real-time odometry, life-long SLAM, and motion planning for robust autonomous navigation. In this paper, we present a novel method aimed at addressing the challenges faced by existing approaches when dealing with scenarios involving significant registration errors. The proposed approach offers a unique solution for removing moving objects without the need for registration, leveraging range flow estimation combined with IMU measurements. To this end, our method performs global range flow estimation by utilizing geometric constraints based on the spatio-temporal gradient information derived from the range image, and we introduce IMU measurements to further enhance the accuracy of range flow estimation. Through extensive quantitative evaluations, our approach showcases an improved performance, with an average mIoU of 45.8%, surpassing baseline methods such as Removert (43.2%) and Peopleremover (32.2%). Specifically, it exhibits a substantial improvement in scenarios characterized by a deterioration in registration performance. Moreover, our method does not rely on costly annotations, which make it suitable for SLAM systems with different sensor setups.

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Visual Inertial 3D Reconstruction System Based on KLT Optical Flow and Voxel Hash

Wei,

Pan,

Fan

et al. 2024

Lecture Notes in Electrical Engineering

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An improved SLAM based on RK-VIF: Vision and inertial information fusion via Runge-Kutta method

Cited by 9 publications

References 21 publications

Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics

Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics

Removing Moving Objects without Registration from 3D LiDAR Data Using Range Flow Coupled with IMU Measurements

Visual Inertial 3D Reconstruction System Based on KLT Optical Flow and Voxel Hash

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