Robot Pose Fusion Using 2D Codes and IMU in Complex Scenes
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Solution Overview
Problem
Current robot positioning methods are prone to inaccuracies in complex scenes with varying lighting and dynamic objects, leading to difficulties in determining the robot's pose accurately.
Innovation Solution
A method utilizing a camera and inertial sensor to obtain two-dimensional code images and sensor data, performing tight coupling optimization and fusion with mileage information to determine the robot's pose, incorporating techniques like OpenCV's solvePnP function and IMU data integration, and using extended Kalman filters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual positioning is used to build maps, then the robot can obtain positioning information, but the positioning accuracy deteriorates in complex scenes with varying lighting and dynamic objects
Solution Approach 1:
The patent combines visual positioning (camera-based 2D code recognition) with inertial positioning (IMU sensor data) to create a fused positioning system. The visual positioning provides absolute position references from 2D codes, while the inertial positioning provides continuous motion information. By merging these two positioning methods through optimization and fusion algorithms, the system achieves accurate pose determination that is robust to lighting changes and dynamic objects in complex scenes.
2Measurement precision
If multiple sensors are integrated for pose determination, then the positioning accuracy in complex scenes is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an optimization module as an intermediary that processes and fuses data from multiple sensors (camera and IMU). This intermediary component coordinates the data from different sensor types, performs tight coupling optimization to resolve inconsistencies, and produces a unified pose estimate. The optimization module acts as a mediator that manages the complexity of multi-sensor integration while enabling accurate pose determination through systematic data fusion.
Data Source
AI summary
A pose determination method and a robot using the same are provided. The method includes: obtaining a two-dimensional code image collected by the camera of the robot and sensor data collected by the sensor of the robot, and determining mileage information of the robot within a predetermined duration, where the sensor data includes an acceleration and an angular velocity, determining a first pose of the camera based on two-dimensional code information recognized from the two-dimensional code image and a pose estimation function, and determining a second pose of the sensor based on the sensor data; obtaining a third pose by performing a tight coupling optimization based on the first pose and the second pose; and obtaining the pose of the robot by fusing the third pose and the mileage information. In such a manner, the accuracy of determining the pose of the robot in a complex scene can be improved.