Robot Localization by Multi-Position Map Matching
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Solution Overview
Problem
Existing robot localization technologies face challenges in achieving accurate pose determination due to uncontrollable drift errors during SLAM processes, especially when robots are moved, suspended, or dragged, leading to inaccurate mapping and localization.
Innovation Solution
A method involving a robot that moves from a current position to a new position during localization, acquiring environment information, and comparing it with a stored environment map to identify its pose, thereby distinguishing similar regional environments and improving localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the robot stays at the original position for localization, then the localization process is simple and quick, but the localization accuracy is insufficient due to similar regional environments and drift errors
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static localization approach (staying at one position) to a dynamic approach (moving to multiple positions). The robot dynamically adjusts its position during the localization process, collecting environment information from multiple locations to improve accuracy while managing the time cost through efficient path planning and information fusion.
2Measurement precision
If the robot moves to multiple positions for localization, then the localization accuracy improves by distinguishing similar environments, but the complexity of the localization process increases
Solution Approach 1:
The patent applies segmentation by dividing the localization process into multiple discrete steps at different positions. Instead of attempting localization from a single complex viewpoint, the system segments the environment observation into multiple simpler observations from different positions, then integrates them to achieve accurate localization.
Solution Approach 2:
The patent implements feedback mechanisms where the robot continuously compares environment information collected at different positions with the stored map, uses the comparison results to refine position estimates, and adjusts subsequent observations based on this feedback to improve localization accuracy.
3Extent of automation
If SLAM technology is used for autonomous localization and navigation, then the robot can navigate autonomously, but uncontrollable drift errors occur when the robot is moved, suspended, or dragged
Solution Approach 1:
The patent applies preliminary action by performing environment information collection at multiple predetermined positions before final localization is determined. This proactive approach of gathering data in advance from multiple locations allows the system to detect and correct drift errors before they significantly impact navigation reliability.
Solution Approach 2:
The system uses feedback by continuously comparing collected environment information with the stored map to detect drift errors, then uses this feedback to correct localization accuracy and maintain reliable autonomous navigation even after unexpected movements.
Data Source
AI summary
Provided is a method for localizing a robot. The robot may move from a current position to a new position during the localizing process, more environment information may be acquired during the new movement, and then the acquired environment information is compared with an environment map stored in the robot, which facilitates successfully localizing a pose of the robot in the stored environment map. In addition, during the movement and localization of the robot, environment information at different positions is generally different, so that similar regional environments may be distinguished, and the problem that an accurate pose cannot be obtained because there may be a plurality of similar regional environments when the robot stays at the original position for localizing may be overcome.