Robot Localization Using Route-Based Exemplar Sampling
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Solution Overview
Problem
Current robot localization methods require extensive onboard memory and processing for storing and processing all sensor data, which increases costs and reduces efficiency, especially when only a subset of data is necessary for accurate localization.
Innovation Solution
A system that generates and provides exemplars, which are selected based on sampling rates determined by sensor data features, allowing robots to request and use only relevant data for localization, reducing storage and processing requirements while maximizing data effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a robot stores all sensor data onboard for localization, then localization accuracy is improved, but onboard memory requirements and costs increase
Solution Approach 1:
The patent extracts only the essential localization features from complete sensor data frames and stores them as exemplars in a centralized database. When a robot needs localization, only the relevant extracted features are retrieved rather than transferring or storing entire data sets. This extraction principle directly reduces onboard memory requirements while preserving localization accuracy.
Solution Approach 2:
The system performs preliminary processing of sensor data to identify and extract key localization features before storage. By pre-processing data to isolate only the features necessary for localization (such as landmark positions, corridor geometries, or distinctive environmental markers), the system eliminates the need for robots to store and process redundant information, thereby reducing memory requirements while maintaining accuracy.
2Measurement precision
If a robot processes all sensor data for localization, then localization accuracy is improved, but processing time and computational demands increase
Solution Approach 1:
The system extracts only the essential localization features from complete sensor data frames and stores them as exemplars in a centralized database. When a robot needs localization, only the relevant extracted features are retrieved rather than transferring or storing entire data sets. This extraction principle directly reduces onboard memory requirements while preserving localization accuracy.
Solution Approach 2:
The system applies partial processing by selectively retrieving only the subset of exemplar data that is relevant to the robot's current location and task, rather than processing all available sensor data. This partial action approach significantly reduces computational time and processing demands while maintaining sufficient accuracy for localization purposes.
3Adaptability or versatility
If a robot stores all sensor data from all locations, then comprehensive localization coverage is improved, but storage requirements and costs increase
Solution Approach 1:
The patent segments the complete sensor data into discrete, location-specific exemplars that are stored in a centralized database. Each exemplar contains only the features relevant to a specific location or region. This segmentation allows the system to provide comprehensive localization coverage across multiple locations while keeping individual storage requirements manageable, as each robot only needs to access exemplars relevant to its current environment rather than storing all possible location data.
Solution Approach 2:
The centralized database serves as a universal repository that can be accessed by multiple robots across different locations. This multi-functional approach allows comprehensive localization coverage to be achieved without each individual robot requiring extensive local storage, as the universal database provides access to location-specific exemplars for any robot that needs them.
4Measurement precision
If a robot uses high sampling rates for exemplar selection, then localization data effectiveness is improved, but data storage requirements increase
Solution Approach 1:
The system dynamically adjusts the sampling rate for exemplar selection based on the specific characteristics of the environment and the robot's current needs. In environments with many distinctive features, the sampling rate can be lower because fewer exemplars are needed for accurate localization. In environments with fewer distinctive features or higher complexity, the sampling rate increases to capture more relevant data points. This dynamic adjustment optimizes the balance between localization effectiveness and storage requirements.
Solution Approach 2:
The system changes the sampling parameter based on environmental conditions, robot velocity, and localization accuracy requirements. By making the sampling rate a variable parameter rather than a fixed value, the system can achieve high localization data effectiveness only when and where necessary, thereby reducing overall storage requirements while maintaining effectiveness in critical scenarios.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for exemplar generation and localization. In some implementations, a method includes obtaining sensor data from a robot traversing a route at a property; determining sampling rates along the route using the sensor data obtained from the robot; selecting images from the sensor data as exemplars for robot localization using the sampling rates along the route; determining that a second robot is in a localization phase at the property; and providing representations of the exemplars for robot localization to the second robot.