Self-Position Estimation Using MCL Reliability Correction
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Solution Overview
Problem
The Monte Carlo localization (MCL) algorithm for estimating a mobile object's self-position is prone to errors when there are gaps between the mapped and actual environments, or increased measurement errors, leading to unreliable position specifications.
Innovation Solution
A method that combines multiple algorithms, including MCL, to determine the reliability of estimated self-positions and correct them using additional algorithms like ORB_SLAM, by generating particles around specified values and comparing reliability between different algorithms to ensure accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the MCL algorithm is used to estimate self-position, then the self-position can be estimated using detection information of sensors, but the reliability of the estimated self-position deteriorates when there are gaps between the map and actual environment or increased measurement errors
Solution Approach 1:
The patent combines multiple self-position estimation algorithms (MCL algorithm and other algorithms) to estimate self-position. By merging the results from multiple algorithms and selecting or correcting based on reliability assessment, the system achieves more reliable and precise self-position estimation compared to using a single algorithm, especially in environments with map gaps or measurement errors.
2Reliability
If multiple algorithms are used to estimate self-position, then the reliability of self-position specification improves, but the device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the reliability of each algorithm's self-position estimation is evaluated, and based on this evaluation, the system selectively corrects or combines results. This feedback-based approach allows the system to manage multiple algorithms efficiently without requiring complex integration logic, thus improving reliability while controlling system complexity.
Data Source
AI summary
In a self-position estimation method, an actual self-position of a mobile object 1 is specified from self-positions each estimated by using a plurality of algorithms. The plurality of algorithms includes an MCL algorithm (11) and an algorithm (12) different from the MCL algorithm. In a case where reliability of an estimated value of the self-position obtained by using the MCL algorithm is low, the estimated value of the self-position obtained by using the MCL algorithm is corrected using an estimated value of the specified self-position or an estimated value of the self-position obtained by using the algorithm (12).