Radar SLAM Circuitry for GPS-Free Mobile Platform Localization
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Solution Overview
Problem
Existing localization and mapping techniques for autonomous mobile platforms, such as vehicles and robots, face challenges in accurately estimating location and orientation, especially in environments with poor GPS reception, and require additional sensors like IMUs, which increase costs and reduce system robustness.
Innovation Solution
A circuitry and method for simultaneous localization and mapping using radar sensors to estimate ego-motion and update a set of particles with occupancy grid maps, eliminating the need for additional sensors by integrating radar detection data for location and orientation estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and IMU sensors are used for localization and mapping, then location and orientation estimation can be provided, but the system may have long-term drift from actual location and increased costs
Solution Approach 1:
The patent implements feedback by using radar detection data to continuously correct and update the particle filter-based location and orientation estimates. The system compares radar-measured positions with predicted positions from the SLAM algorithm and uses this feedback to adjust particle weights and resample, thereby correcting long-term drift that would otherwise accumulate in GPS/IMU-based systems.
Solution Approach 2:
The patent replaces the mechanical sensor system (GPS and IMU hardware) with a radar-based electromagnetic wave system for localization and mapping. By using radar sensors to emit and receive electromagnetic waves for measuring positions of stationary objects, the system eliminates the need for separate GPS and IMU sensors while achieving more reliable long-term location accuracy without drift.
2Measurement precision
If extra sensors such as IMUs are used to provide initial prediction of pose in SLAM, then prediction accuracy is improved, but system costs increase
Solution Approach 1:
The patent makes the radar sensor multi-functional by using it for both detection of stationary objects and estimation of ego-motion for SLAM initialization. The same radar detection data that is used for mapping the environment is also processed to determine vehicle velocity and position changes, thereby providing pose prediction without requiring separate IMU sensors.
Solution Approach 2:
The system makes the radar sensor self-sufficient by enabling it to perform multiple functions independently. The radar not only detects stationary objects for mapping but also autonomously provides ego-motion information for SLAM initialization through analysis of detection data over time, eliminating dependency on additional sensors.
3Measurement precision
If landmark extraction and matching is performed in SLAM implementations, then localization accuracy is improved, but computational cost and processing time increase
Solution Approach 1:
The patent extracts only the essential information needed for localization by directly using radar detection data of stationary objects without performing full landmark extraction and matching. The system extracts position and velocity information directly from radar returns and uses this simplified data for particle filter updates, eliminating computationally expensive image processing and feature matching steps.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies data acquisition and synchronization, enhances system robustness, and improves mapping accuracy by continuously updating location and orientation estimates using radar-based ego-motion and occupancy grid maps.
Implementation Method 1
radar sensors are known which can measure a relative (radial) velocity of an object, a relative (radial) velocity of an object and a distance to the object
Implementation Method 2
based on a time-of-flight and a Doppler shift of the reflected electromagnetic wave, radar sensors can provide radar detection data indicating an estimation of the relative (radial) velocity of the object and the distance to the object
Implementation Method 3
based on a time-of-flight and a Doppler shift of the reflected electromagnetic wave
Data Source
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Figure 3A~3C
AI summary
A circuitry for simultaneous localization and mapping for a mobile platform, wherein the circuitry is configured to: estimate, based on obtained radar detection data, an ego-motion of the mobile platform; and update, based on the estimated ego-motion and the obtained radar detection data, a set of particles, wherein each particle of the set of particles includes a location and an orientation of the mobile platform and an occupancy grid map that represents an environment of the mobile platform, wherein the occupancy grid map includes a plurality of cells and each cell of the plurality of cells is assigned an occupation probability which indicates a probability that the cell is occupied by a target.