Improved nonlinear optimization method of monocular inertial navigation SLAM

A nonlinear optimization, single-purpose technology, applied in the direction of navigation, navigation, mapping and navigation through velocity/acceleration measurement, can solve the problems of IMU pre-integration, nonlinear optimization, sensor time synchronization, etc., to achieve good scale factor, accuracy and robustness improvement, the effect of solving the scale uncertainty problem

Pending Publication Date: 2020-01-24
BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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Problems solved by technology

However, the integration of IMU into monocular SLAM will make the system more complicated. IMU has measurement noise, acceleration and angular velocity bias (bias) in the measurement process, which leads to a lot of new problems,...

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  • Improved nonlinear optimization method of monocular inertial navigation SLAM
  • Improved nonlinear optimization method of monocular inertial navigation SLAM
  • Improved nonlinear optimization method of monocular inertial navigation SLAM

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Embodiment Construction

[0025] The present invention will be further described below in combination with specific embodiments and accompanying drawings.

[0026] Aiming at the current pure visual SLAM (Simultaneous Localization and Mapping) system is easily affected by scenes such as ambient lighting, texture, and dynamic environment, and the monocular SLAM system also has the problem of blurred initialization scale, the present invention uses a monocular camera and an IMU ( Inertial Measurement Unit) fusion method effectively solves the scale ambiguity problem existing in the monocular SLAM system, and the fusion of these two sensors significantly improves the robustness of the system and can obtain higher positioning accuracy. The present invention is based on the VINS-mono system with the best monocular vision and IMU fusion performance at present, improves the initialization scheme, increases the initialization of the acceleration bias deviation, and makes it suitable for low-cost IMU sensors. Th...

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Abstract

The invention discloses an improved nonlinear optimization method of monocular inertial navigation SLAM. On the basis of the VINS-Mono engineering, the nonlinear optimization method is used for coupling IMU data in a tight coupling manner. The method comprises the steps of: image and IMU preprocessing; initialization; back-end sliding window optimization; and closed-loop detection and optimization. According to the improved nonlinear optimization method disclosed by the invention, algorithm improvement is performed based on the existing VINS.mono, a test is performed on a public data set EuRoC, and contrastive analysis is performed on the previous VINS.mono scheme. The results show that the IMU initialization can well restore a scale factor of the system, solve the problem of monocular SLAM scale uncertainty, can complete the initialization faster and more accurately, and can robustly perform real-time motion estimation and sparse map construction, and the accuracy and the robustness of the improved system are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of robot vision positioning and navigation, in particular to an improved nonlinear optimization monocular inertial navigation SLAM method. Background technique [0002] Simultaneous Localization and Mapping (SLAM for short) technology is an important direction of mobile robot positioning and navigation technology. It refers to the positioning of moving objects only relying on the sensors carried by them during the movement process, and at the same time constructing maps of the surrounding environment. . Since cameras are cheaper and smaller than laser sensors, and the acquired images contain richer information, vision-based SLAM (V-SLAM, referred to as visual SLAM) technology has become a research hotspot; The binocular camera and RGB-D depth camera, the monocular camera has the advantages of low power consumption, small size, and light weight, especially on the platform of small mobile robots (such as micr...

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Application Information

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IPC IPC(8): G01C21/00G01C21/16
CPCG01C21/005G01C21/165
Inventor 危双丰刘振彬
Owner BEIJING UNIV OF CIVIL ENG & ARCHITECTURE
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