Self-Position Estimation Using Slope-Filtered Map Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The accuracy of position estimation on a two-dimensional map is reduced due to the difference between oblique and horizontal distances in slope sections, affecting the precision of self-positioning for moving bodies.
Innovation Solution
A self-position estimation method that detects the relative position of targets around a moving body, corrects it based on movement data, and accumulates this information to selectively collate target position data with map information, excluding data from slope sections to minimize estimation errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If target position data from all sections is used for position estimation, then the quantity of data for estimation is increased, but the accuracy of position estimation is reduced due to slope-induced errors
Solution Approach 1:
The patent segments the accumulated target position data based on slope amount, dividing data into slope sections (slope amount ≥ threshold) and non-slope sections (slope amount < threshold). This segmentation allows selective use of data from non-slope sections for position estimation, eliminating slope-induced errors while maintaining sufficient data quantity for accurate estimation.
Solution Approach 2:
The patent applies local quality by assigning different qualities to different portions of target position data based on the slope characteristics of their respective sections. Data from non-slope sections is designated as high-quality (usable) while data from slope sections is designated as low-quality (excluded), thereby improving overall estimation accuracy by prioritizing reliable data.
2Reliability
If all accumulated target position data is collated with map information, then the completeness of position estimation is improved, but the reliability is reduced due to inclusion of erroneous data from slope sections
Solution Approach 1:
The patent performs preliminary action by pre-classifying target position data into slope and non-slope sections based on slope amount before the actual position estimation process. This preliminary classification stores the slope amount information alongside each target position data, enabling rapid and reliable selection of valid data during estimation without adding significant complexity to the overall system.
Data Source
AI summary
A self-position estimation method includes: detecting a relative position of each target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a slope amount of a traveling road of the moving body; selecting, from among the accumulated target position data, the target position data of one or more targets in one or more sections having a slope amount less than a threshold value; and collating the selected target position data with map information indicating positions of the targets on a two-dimensional map to estimate a present position of the moving body.