Mobile Self-Position Estimation with Intermittent SLAM Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The SLAM algorithm for estimating a mobile object's self-position in a global coordinate system is prone to errors due to initial position errors and integration of movement amounts, which can impair the reliability of the specified self-position.
Innovation Solution
A self-position estimation method that combines multiple algorithms, including SLAM and a different algorithm like AMCL or ORB_SLAM, with a correction step to intermittently correct the SLAM-estimated self-position using values from the other algorithm and a specified self-position, utilizing a weighting factor learning process and correction parameters to maintain reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the SLAM algorithm is used to estimate self-position by sequentially calculating movement amounts, then the self-position can be estimated in a global coordinate system, but the error of the self-position increases in an integration manner due to initial position errors and errors of sequentially calculated movement amounts
Solution Approach 1:
The patent applies feedback by using the detected self-position (from sensors like odometry or external sensors) to correct the estimated self-position obtained by the SLAM algorithm. The correction amount is calculated based on the difference between the detected self-position and the estimated self-position, and this correction is applied to reduce cumulative errors in the SLAM estimation, thereby improving reliability while maintaining measurement precision.
Solution Approach 2:
The patent performs preliminary correction of the SLAM estimated self-position by using detection results from other sensors before final position specification. The correction amount is calculated in advance based on the difference between detected self-position and SLAM estimated position, and this pre-calculated correction is applied to prevent error accumulation from affecting the final position estimate.
2Adaptability or versatility
If multiple algorithms including SLAM are used to estimate self-position, then the estimation can be performed using detected information from multiple sensors, but the complexity of the system increases
Solution Approach 1:
The patent merges multiple self-position estimation approaches by combining the SLAM algorithm with other estimation methods (such as odometry-based or external sensor-based estimation). The system integrates results from different algorithms and sensors, using a correction mechanism that combines SLAM estimated positions with detected positions from other sources, thereby achieving versatile adaptability while managing system complexity through unified correction processing.
Data Source
AI summary
A method capable of appropriately estimating (specifying) a self-position of a mobile object while appropriately correcting an estimated value of a self-position by an SLAM algorithm is provided. In a self-position estimation method, an actual self-position of a mobile object 1 is specified (fixed) from self-positions estimated by a plurality of algorithms. The plurality of algorithms includes an SLAM algorithm (12) and an algorithm (11) different from the SLAM algorithm. A correction processing unit 16 intermittently corrects an estimated value of a self-position obtained by the SLAM algorithm in accordance with any one self-position out of an estimated value of a self-position obtained by an algorithm other than SLAM and a specified self-position.