Mobile Object Position Estimation With Posture-Weighted Feature Points
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Solution Overview
Problem
Conventional positioning systems fail to accurately estimate the position of mobile objects due to the lack of consideration for the most recent posture of distance measurement sensors, leading to inaccuracies in position estimation.
Innovation Solution
A position estimation device that extracts feature points from distance measurement data, calculates posture information using a SHOT method, determines weights based on feature point restrictions, and minimizes differences between feature points and corresponding map data to estimate the position of the mobile object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional positioning systems use feature points from distance measurement data without considering sensor posture, then the system is simpler to implement, but the position estimation accuracy deteriorates
Solution Approach 1:
The patent changes the parameter of feature point selection by introducing posture-based weighting. Instead of uniformly using all feature points, the system calculates weights based on the distance measurement sensor's posture and the restriction directions of feature points, thereby improving position estimation accuracy while accounting for sensor orientation changes.
Solution Approach 2:
The patent performs preliminary calculation of restriction information and weights before position estimation. By pre-calculating the posture of the distance measurement sensor and determining weights for each feature point based on restriction directions, the system prepares optimized input data that improves subsequent position estimation accuracy.
2Measurement precision
If the system assigns equal weights to all feature points, then the processing is simpler, but the position estimation accuracy in environments with biased feature distributions deteriorates
Solution Approach 1:
The patent applies local quality by assigning different weights to different feature points based on their individual restriction directions and the sensor's posture. Instead of uniform weighting, each feature point receives a weight reflecting its reliability and relevance to the current sensor orientation, which improves accuracy in environments with biased feature distributions.
Solution Approach 2:
The system dynamically changes the weight parameter for each feature point based on calculated restriction information. By adjusting these weights according to the sensor's posture and feature point characteristics, the system optimizes position estimation accuracy for specific environmental conditions.
3Reliability
If the system does not calculate restriction information for feature points, then the processing time is shorter, but the ability to prevent local solutions and improve data association deteriorates
Solution Approach 1:
The patent performs preliminary calculation of restriction information for each feature point before the main position estimation process. By pre-determining the restriction directions and using this information to calculate weights, the system improves data association reliability while organizing processing in an efficient sequence.
Data Source
AI summary
A position estimation device that estimates a position of a mobile object provided with a distance measurement sensor, includes processing circuitry to extract feature points from distance measurement data outputted from the distance measurement sensor to estimate restriction information indicating a direction in which posture assumed by a feature value of each of the feature points is restricted; to determine weights, to be respectively assigned to the feature points, based on the restriction information; and to calculate a rotation amount and a translation amount of the distance measurement data so that a sum of values obtained by respectively multiplying evaluation values representing differences by the weights is minimized, the differences being differences between the feature points of the distance measurement data and corresponding points respectively corresponding to the feature points in stored map information or previously measured distance data, and to estimate the position of the mobile object.