SLAM method for multi-sensor fusion in dynamic scene
By constructing a multi-sensor fusion SLAM method in dynamic scenarios, the combination of visual inertial subsystem and laser inertial subsystem is used to solve the problem of low positioning accuracy in dynamic environments, and higher positioning accuracy and robustness are achieved.
CN119845247AActive Publication Date: 2025-04-18HEFEI UNIV OF TECH
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Patent Information
- Application Number
- CN202411910689.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-04-18
- Estimated Expiration
- 2044-12-24
AI Technical Summary
Technical Problem
The prior art has problems of inaccurate dynamic identification and insufficient fusion in dynamic environments, resulting in low positioning accuracy.
Method used
A multi-sensor fusion SLAM method is proposed in dynamic scenarios. By constructing a visual inertial subsystem and a laser inertial subsystem, it processes dynamic and static environment information respectively, and closely couples the data of vision, laser and inertial measurement units between the two subsystems to optimize the robot position and global map.
Benefits of technology
It improves the positioning accuracy and robustness of the system, and can accurately identify and process dynamic objects in a dynamic environment, reducing the problems of light changes and limited environmental perception range.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a multi-sensor fusion SLAM (Simultaneous Localization and Mapping) method in a dynamic scene, which belongs to the field of robot perception and comprises the following steps of: preprocessing visual, laser and IMU (Inertial Measurement Unit) data, including time synchronization and space synchronization; a sensor mathematical model is established, a visual inertia subsystem and a laser inertia subsystem are constructed at the same time, and the two systems are fused in a tight coupling mode. The laser inertial subsystem assists the visual inertial subsystem to complete scale recovery, and the visual inertial subsystem assists the laser inertial subsystem to carry out loopback detection. And finally, establishing a global nonlinear optimized factor graph model in a graph optimization mode, and adding a laser odometer factor, an IMU pre-integration factor, a visual factor and a loopback detection factor into a factor graph to obtain global optimal pose estimation and a three-dimensional map. In a dynamic environment, information of a plurality of sensors is fused, so that the influence of a dynamic object on positioning is reduced, and higher positioning precision and robustness are achieved.
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