Locus Correction Nodes Probabilistic Evaluation Mobile Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As a mobile object travels, measurement errors in its locus data accumulate, leading to inconsistencies in mapping, especially when revisiting previous spots or using multiple vehicles, resulting in decreased map accuracy and potential loss of location awareness.
Innovation Solution
A locus correcting method that sets nodes on measurement data, correlates position data from different units, represents positions probabilistically, and calculates an evaluation function to determine the most likely locus based on these probabilities, thereby correcting the traveling path and maintaining consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If map is made sequentially in time by matching surrounding object shape data, then map generation is simple and efficient, but cumulative error increases as the map region extends
Solution Approach 1:
The patent segments the locus correction problem into discrete nodes along the traveling path. Each node represents a specific position where measurement data is correlated, allowing the system to handle large-scale mapping by breaking it into manageable segments while maintaining overall accuracy through probabilistic evaluation across all nodes
Solution Approach 2:
The patent implements feedback by calculating an evaluation function that uses probability distributions of position data to assess and correct locus deviations. The system continuously evaluates the likelihood of nodes occurring at specific positions and adjusts the locus accordingly, creating a self-correcting mechanism that prevents cumulative error
2Measurement precision
If multiple measurement units are used to acquire position data, then locus accuracy can be improved, but system complexity and data correlation difficulty increase
Solution Approach 1:
The patent introduces nodes as intermediary elements that mediate between multiple measurement units and the final locus calculation. Each node serves as a reference point that correlates position data from different measurement units through probability distributions, simplifying the integration of multi-source data while maintaining accuracy
Solution Approach 2:
The patent transforms position data from multiple measurement units into probability distributions representing the likelihood of nodes occurring at specific positions. This parameter transformation allows the system to handle measurement uncertainties and variations from different units through statistical evaluation rather than direct deterministic correlation
3Area of stationary object
If the same spot is stored as different spots due to measurement errors, then map coverage increases, but map consistency and accuracy deteriorate
Solution Approach 1:
The patent uses feedback through the evaluation function to detect and correct inconsistencies where the same spot might be represented as different locations. By evaluating the probability of node occurrences across the entire map, the system identifies and reconciles duplicate representations, maintaining consistency while preserving comprehensive coverage
Data Source
AI summary
Calculating a highly accurate locus is a goal. An administrative part (20) for correcting a traveling locus of a mobile object (V) is characterized by including an evaluation function generating unit (21) that sets a plurality of nodes on locus data of the mobile object (V) acquired by a wheel rotation quantity measuring unit (16), correlates position data of the mobile object (V) acquired by the wheel rotation quantity measuring unit (16) to the nodes and correlates position data of the mobile object (V) acquired by other units of measurement means than the wheel rotation quantity measuring unit to the nodes, represents positions at which the nodes may occur by probability, represents positions at which position data correlated to the nodes may occur by probability, and calculates an evaluation function including the nodes and the position data as variables, based on each probability; and a locus optimization calculation unit (22) that calculates a locus on which each node occurs with largest probability, based on the evaluation function.