Robot positioning and map construction system based on binocular vision features and IMU information

Abstract

Disclosed is a robot positioning and map construction system based on binocular vision features and IMU information, comprising a binocular information collection, feature extraction and matching module, an improved IMU initialization and motion module, a visual SLAM algorithm initialization and tracking module, a local mapping module and a loop detection and optimization module. The binocular information collection, feature extraction and matching module comprises a binocular ORB feature extraction sub-module, a binocular feature matching sub-module and an IMU information collection sub-module. The improved IMU initialization and motion module includes an IMU angular rate deviation estimation sub-module, a gravity acceleration prediction sub-module, an IMU acceleration deviation estimation sub-module and an IMU pre-integration sub-module. The visual SLAM algorithm initialization and tracking module includes a tracking inter-frame motion sub-module and a key frame generation sub-module. The local mapping module includes a new key frame insertion sub-module, a local BA optimization sub-module and a redundant key frame elimination sub-module. The loop detection and optimization module includes a loop detection sub-module and a global optimization sub-module. The invention provides a robot positioning and map construction system based on binocular vision features and IMU information, which has good robustness, high accuracy and strong adaptability.

Description

technical field [0001] The invention belongs to the technical field of robots, and in particular relates to a robot positioning and map construction system. Background technique [0002] Simultaneous Localization and Mapping (SLAM) technology is an important problem in the field of robot navigation. The SLAM problem can be described as: the robot can build a global map of the explored environment, and at the same time, it can use this map to infer at any time. own position. The robot moves freely in the environment through the sensor equipment, locates its own position through the collected information, and builds a map on the basis of the positioning, so as to realize the simultaneous positioning and mapping of the robot. There are two main factors that affect the solution of the SLAM problem, namely, the data characteristics of the sensor and the correlation of the observation data. If the robustness and accuracy of the data association can be improved and the utilization...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the disadvantages of poor robustness, low accuracy and poor adaptability of existing robot positioning and map construction systems, the present invention provides a binocular-based Robot localization and map construction system based on visual features and IMU information

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