Improved nonlinear optimization method of monocular inertial navigation SLAM

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...

AI Technical Summary

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, including: different sensor time Synchronization problems, initialization problems of system state variables, IMU pre-integration problems, nonlinear optimization problems, closed-loop detection and global optimization problems, etc.

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