Mobile Robot Localization Using Composite Position Data
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently and accurately localizing within diverse sites, particularly due to limitations in data types and reliability thresholds, leading to inaccuracies and inefficiencies in navigation.
Innovation Solution
The method involves obtaining satellite-based position data and combining it with odometry data or point cloud data to generate composite data, which is then used for localization. This approach filters data based on reliability thresholds and associates each position of the robot with relevant data types to enhance localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data types (satellite-based position data, odometry data, point cloud data) are integrated for localization, then localization accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple data types (satellite-based position data, odometry data, point cloud data) into a unified localization framework. The system integrates these diverse data sources through a common processing architecture that fuses their information to achieve higher localization accuracy than any single data type could provide alone.
Solution Approach 2:
The localization system is designed with multi-functionality to handle various data types universally. The system can process satellite-based position data, odometry data, and point cloud data through a single integrated framework that adapts to different data formats and sources, reducing the need for separate processing pipelines.
2Reliability
If data filtering based on reliability thresholds is applied, then localization reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering of data based on reliability thresholds before full localization processing. By pre-evaluating and filtering out unreliable data points beforehand, the system reduces the computational burden during main processing and avoids wasting time on obviously poor quality data.
Solution Approach 2:
The system dynamically adjusts reliability thresholds and filtering parameters based on environmental conditions and data quality assessments. This allows the system to optimize the balance between filtering effectiveness and processing speed, adapting to different operational scenarios.
Data Source
AI summary
Systems and methods are described for instructing performance of a localization and an action by a mobile robot based on composite data. A system may obtain satellite-based position data and one or more of odometry data or point cloud data. The system may generate composite data by merging the satellite-based position data and the one or more of the odometry data or the point cloud data. The system may instruct performance of a localization by the mobile robot based on the composite data. Based on the localization by the mobile robot, the system may identify an action and instruct performance of the action by a mobile robot.