Adaptive Particle Filter Localization for Dynamic Map Distortions
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Solution Overview
Problem
Accurate localization of mobile automation apparatuses in complex environments is hindered by moving obstacles and map distortions, which existing technologies struggle to address effectively.
Innovation Solution
A method and apparatus that utilize a particle filter-based localization mechanism, generating candidate poses within an environmental map, updating these poses based on motion data, and adjusting a resampling threshold based on localization confidence levels to improve positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed resampling threshold is used in particle filter localization, then the system is simple to implement, but localization accuracy deteriorates in dynamic environments with moving obstacles and map distortions
Solution Approach 1:
The patent implements a dynamic resampling threshold that adapts based on localization confidence levels. The threshold is adjusted in real-time according to the consistency between predicted and actual sensor observations, allowing the system to maintain high localization accuracy in dynamic environments with moving obstacles and map distortions without requiring overly complex fixed-threshold mechanisms
2Measurement precision
If candidate poses are frequently resampled to improve accuracy, then localization precision improves, but computational time and processing load increase
Solution Approach 1:
The system employs feedback through localization confidence level calculation, comparing predicted sensor readings with actual observations. This feedback mechanism determines when resampling is necessary, allowing the system to achieve high localization precision only when needed rather than continuously, thereby reducing unnecessary computational time and processing load
3Reliability
If the particle filter uses a high number of particles to improve localization robustness, then measurement precision improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by using a moderate number of particles combined with confidence-based selective resampling. Instead of continuously using a large number of particles, the system only intensifies particle processing when localization confidence is low, achieving sufficient robustness while conserving computational resources and energy
Data Source
AI summary
A method for localization of a mobile automation apparatus includes, at a navigational controller: generating a set of candidate poses within an environmental map; updating the candidate poses according to motion data corresponding to movement of the mobile automation apparatus; receiving observational data collected at an actual pose of the mobile automation apparatus; generating (i) respective weights for the candidate poses, each weight indicating a likelihood that the corresponding candidate pose matches the actual pose, and (ii) a localization confidence level based on the weights; responsive to determining whether the localization confidence level exceeds a candidate pose resampling threshold: when the determination is affirmative, (i) increasing the candidate pose resampling threshold and (ii) generating a further set of candidate poses; and when the determination is negative, (i) decreasing the candidate pose resampling threshold without generating the further set; and repeating the updating, the receiving, the generating and the determining.