Mobile Robot Self-Position Updating Across Changing Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing self-position estimation techniques for mobile robots, such as SLAM and odometry, struggle to maintain accuracy in changing environments, leading to degraded matching accuracy and consistency.
Innovation Solution
An information processing device equipped with a detection section and a control section that includes a storage, a map producer, self-position estimators for existing and current maps, a reliability evaluator, and a self-position updater. This device updates self-positions based on reliability evaluations, ensuring consistency across different maps and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-position estimation is performed by matching detection information with an existing map prepared in advance, then positioning can be performed using pre-existing map data, but matching accuracy is degraded when the environment has changed
Solution Approach 1:
The system dynamically switches between two self-position estimation methods (existing map matching and current map matching) based on environmental conditions. The reliability evaluation unit assesses whether the environment has changed and selects the appropriate estimation method, making the system adaptive rather than static.
Solution Approach 2:
The system changes the parameter being used for matching - switching between existing map data and current map data based on environmental conditions. When the environment is stable, existing map matching is used; when changes are detected, current map matching is employed instead.
2Reliability
If multiple self-position estimation methods are used to improve accuracy in changing environments, then reliability is improved, but system complexity increases
Solution Approach 1:
The self-position estimation function is segmented into two distinct estimation units: an existing map self-position estimation unit and a current map self-position estimation unit. Each unit handles specific scenarios, and a reliability evaluation unit determines which unit's result to trust, dividing the complex problem into manageable parts.
Solution Approach 2:
The reliability evaluation unit acts as an intermediary that assesses the trustworthiness of each estimation method's output and selects the appropriate self-position information. This mediator coordinates between the two estimation methods without requiring complex integration logic.
Data Source
AI summary
Provided are an information processing device and a mobile robot capable of ensuring the accuracy of estimation of a self-position even with an environmental change and maintaining the consistency of the self-position on a map. The information processing device of the mobile robot includes a control section and a detection section configured to detect a distance to a peripheral object and the direction thereof as detection information. The control section includes a storage, a map producer configured to produce a peripheral map, an existing map self-position estimator configured to estimate a current self-position based on an existing map, a current map self-position estimator configured to estimate the current self-position based on a current map, a reliability evaluator configured to evaluate the reliability of each of the estimated self-positions, and a self-position updater configured to update either one of the self-positions based on the reliability.