Radar Sensor Landmark Matching for Real-Time Low-Power Localization
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Solution Overview
Problem
Conventional radar localization and odometry systems are too slow for real-time control of vehicles like autonomous vehicles and landcraft or watercraft due to high computational requirements, and they lack sufficient accuracy and precision for precise pose estimation and navigation.
Innovation Solution
A computer-implemented method for localizing a radar sensor by obtaining radar scans, extracting landmarks, computing descriptors, matching them with reference sets, and localizing the sensor's location using descriptor matching, optimized for low-power hardware with techniques like dimensionality reduction and caching to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional radar localization and odometry systems are used, then measurement precision is improved, but processing speed deteriorates making them too slow for real-time control
Solution Approach 1:
The patent segments the localization problem into distinct modules: landmark extraction from radar scans, descriptor computation for extracted landmarks, descriptor matching against reference sets, and pose estimation. This modular segmentation allows each component to be optimized independently for both accuracy and speed, enabling real-time processing while maintaining precision.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing reference landmark sets and their descriptors in advance. During real-time operation, the system only needs to extract current landmarks, compute their descriptors, and match them against the pre-prepared reference sets, significantly reducing processing time while maintaining localization accuracy.
2Productivity
If high computational power is used to improve processing speed, then productivity is improved, but use of energy worsens making it unsuitable for low-power platforms
Solution Approach 1:
The patent extracts and processes only the essential features from radar scans - specifically extracting landmarks and their descriptors rather than processing complete point clouds. This selective extraction reduces computational load and energy consumption while maintaining the ability to achieve real-time localization on low-power platforms.
Solution Approach 2:
The patent applies partial action by processing only a subset of radar data - specifically focusing on extracted landmarks rather than all detected objects. This partial processing approach reduces computational requirements and energy usage while still providing sufficient localization accuracy for real-time control applications.
3Reliability
If conventional landmark extraction methods are used, then reliability is improved, but device complexity increases making implementation difficult on embedded systems
Solution Approach 1:
The patent replaces complex mechanical/computational landmark extraction systems with a simplified descriptor-based matching approach. Instead of using sophisticated geometric processing or machine learning models, the system extracts landmarks and matches them using computed descriptors against reference sets, reducing device complexity while maintaining reliability for embedded implementation.
Data Source
AI summary
A computer-implemented method of localizing a radar sensor, the method comprising: obtaining a first radar scan of a first environment of the radar sensor, wherein the first radar scan comprises a set of power-range spectra, including a first power-range spectrum; extracting a first set of landmarks, including a first landmark, from the first radar scan, wherein the first landmark is defined by a range and an azimuth; computing a respective first set of descriptors, including a first descriptor, of the first set of landmarks, wherein the first descriptor defines the first landmark by respective relative ranges and azimuths in relation to one or more landmarks included in the first set of landmarks; accessing one or more reference sets of landmarks of respective environments and computing respective reference sets of descriptors of the reference sets of landmarks; matching the first set of descriptors to a corresponding first reference set of descriptors; and localizing a first location of the radar sensor using a first result of the matching.