An AR Projection Improvement Strategy via the Integration of Target Detection and ORB-SLAM2
- Resource Type
- Conference
- Authors
- Wang, Chen; Hu, Yuanqi
- Source
- 2021 IEEE 3rd International Conference on Circuits and Systems (ICCS) Circuits and Systems (ICCS), 2021 IEEE 3rd International Conference on. :277-282 Oct, 2021
- Subject
- Components, Circuits, Devices and Systems
Visualization
Simultaneous localization and mapping
Circuits and systems
Conferences
Object detection
Feature extraction
Cameras
Projection
Augmented Reality (AR)
Simultaneous Localization and Mapping (SLAM)
ORB-SLAM2
- Language
Visual simultaneous localization and mapping (vSLAM) algorithms are mainstream technical methods for markerless augmented reality using monocular cameras. However, most vSLAM algorithms are incompetent to project virtual object on a specified plane, especially when rigour precision is required. This is because they fail to distinguish the mappoints of interest from other normal ones. In this work we propose a new SLAM system which integrates target detection algorithm into conventional ORB-SLAM2 so that mappoints of interest can be regarded as variables and hence be optimized continuously. Specifically, the proposed system adds a new class member called Target in the map of ORB-SLAM2 so that the system can detect the target during operation with Oriented Fast and Rotated Brief (ORB) features and distinguish the Target Mappoints from others. Compared to conventional ORB-SLAM2, proposed system needs to take on two more tasks: management of Target Mappoints and updating the projection matrix in all three treads. In this work we use the Lena picture as the target plane we want to project visual objects on and the test results demonstrate that our system can perform projection more accurately.