Robot Positioning Using Semantic Scene-Based Sensor Selection
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Solution Overview
Problem
Existing robot positioning methods lack accuracy and efficiency, particularly in dynamic and complex environments, leading to suboptimal navigation and maintenance challenges.
Innovation Solution
A robot positioning method utilizing a camera and various sensors to extract semantic information, align coordinate systems, and determine target sensing data based on the scenario, enhancing the accuracy of pose determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensing data is used for robot pose determination, then comprehensive information is available, but positioning accuracy decreases due to irrelevant data in dynamic environments
Solution Approach 1:
The patent extracts only the relevant sensing data needed for pose determination by identifying correspondences between current and historical sensing data. This extraction principle filters out irrelevant data from dynamic environments, improving positioning accuracy while reducing processing complexity.
Solution Approach 2:
The patent applies different processing strategies to different types of sensing data based on their relevance to pose determination. By treating static and dynamic environment data differently, the system optimizes positioning accuracy without unnecessary processing of irrelevant information.
2Measurement precision
If semantic information extraction is added to the positioning process, then positioning accuracy improves through scenario understanding, but processing time increases
Solution Approach 1:
The patent performs preliminary extraction of semantic information from sensing data to identify the scenario type (e.g., indoor, outdoor, crowded). This preliminary action enables the system to select appropriate positioning strategies in advance, improving accuracy while managing processing time through proactive scenario recognition.
3Measurement precision
If coordinate system alignment is performed between camera and sensors, then positioning accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces a coordinate system alignment mechanism that acts as an intermediary between the camera and other sensors. This mediator performs necessary transformations to ensure coordinate consistency across different sensing modalities, improving positioning accuracy while managing the complexity through a unified transformation framework.
Data Source
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AI summary
Provided are a robot positioning method and apparatus, an intelligent robot, and a storage medium. The method includes: configuring a camera and various sensors on a robot so that the robot may acquire an image collected by the camera and various sensing data collected by the various sensors (step 101); next extracting semantic information contained in the collected image (step 102) and identifying, according to the semantic information, a scenario where the robot is currently identified (step 103); finally, determining a current position of the robot according to target sensing data corresponding to the scenario where the robot is located (step 104). In the method, the sensing data used during determining the pose of the robot is not all the sensing data, but is the target sensing data corresponding to the scenario. Therefore, the basis for determining the pose is more targeted, thus further improving the accuracy of the pose.