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Weighted Euler pre-integration method in visual inertia odometer

A pre-integration and odometer technology, which is applied in the field of weighted Euler pre-integration, can solve the problems of loss of accuracy and influence of positioning result accuracy

Active Publication Date: 2018-11-02
SOUTHEAST UNIV
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Problems solved by technology

The existing pre-integration technology usually uses the numerical integration method of Euler integral and median integral. Although it is easy to calculate, it loses part of the accuracy and affects the accuracy of the final positioning result.

Method used

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  • Weighted Euler pre-integration method in visual inertia odometer
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  • Weighted Euler pre-integration method in visual inertia odometer

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[0125] Example: The experimental data is derived from the publicly available EuRoC MAV dataset. The seven test datasets are V1-01-easy, V1-02-mediunm, V2-01-easy, V2-02-medium, MH-01-easy, MH-03-medium and MH-05-difficult. These datasets demonstrate the dynamic motion of a drone equipped with an IMU and a stereo camera, using a drone such as the attached figure 2 shown. The VIO system is based on Visua-Inertial ORB-SLAM2.

[0126] The estimated trajectories of these five datasets are first compared with the ground truth trajectories. image 3 It is shown that the estimated trajectory almost tracks the ground truth. The dotted line exists because the VIO scheme used is based on the keyframe method. If the duration between two keyframes you pick is long, the track between the two keyframes will become a dashed line. But the overall estimated trajectory is in line with reality.

[0127] Next compare the estimated biases of the gyroscopes and accelerometers to the actual bia...

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Abstract

The invention discloses a weighted Euler pre-integration method in a visual inertia odometer (VIO). Pre-integration of an inertia measurement unit (IMU) is an important data preprocessing part in a VIO processing flow, and improvement of the precision of the pre-integration has important significance for subsequent joint initialization, motion estimation and optimization. The method disclosed by the invention comprises the following steps: using weighted Euler pre-integration on an integral motion model, performing iterative summation processing on the IMU measured value between two key framesso as to obtain constraint of motion between the two key frames, and performing formula manipulation to further obtain a relative motion increment model and a pre-integration measurement model. Compared with the traditional pre-integration method, the method disclosed by the invention has the advantages that the measured angular velocity and accelerated velocity measured values can be fully utilized, and the variation tendency of the velocity and angle can be really reflected. A EuRoc MAV data set is used in a vision and inertial device tight coupling system, the experimental result is ideal,the positioning accuracy is obviously improved, and the RMSE (Root Mean Square Error) is improved by about 40%.

Description

technical field [0001] The invention relates to a weighted Euler pre-integration method in a visual-inertial odometer, and belongs to the technical field of simultaneous localization and map construction (SLAM). Background technique [0002] Visual-inertial odometry (VIO) is a fusion SLAM system that fuses visual and inertial sensors. Both visual sensors and inertial sensors are low in cost and contain a lot of information. They are complementary in many ways. VIO has potential and extensive applications in many fields, such as robotics, augmented reality, and driverless driving. [0003] A typical and efficient fusion method in monocular VIO is tight coupling. This method combines the amount of vision and inertia to be optimized into one parameter for overall estimation. Inertial measurement units (IMUs) typically acquire measurement data at a rate much higher than vision sensors. The current VIO solution can obtain higher-precision state estimation through nonlinear opti...

Claims

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Application Information

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IPC IPC(8): G01C22/00G06T7/73
CPCG01C22/00G06T7/73G06T2207/10016
Inventor 潘树国曾攀王帅黄砺枭
Owner SOUTHEAST UNIV