Vehicle Pose Estimation via Radar Occupancy Grid Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle pose determination systems, reliant on odometry- and GPS-based equipment, suffer from errors that accumulate over time, leading to undesirable drifts in location and orientation data, and are costly to improve with carrier-phase enhancement GPS.
Innovation Solution
A vehicle pose determining method using sensor fusion that combines dynamics sensor data with radar sensor data to generate occupancy grids, which are then used to derive correction data and improve the accuracy of vehicle pose estimation, reducing errors and drift through mathematical optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If carrier-phase enhancement GPS equipment is used to improve vehicle pose accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple existing sensor types (odometry sensors, GPS receivers, and radar sensors) into an integrated pose determination system. By merging these sensors and fusing their data through occupancy grid processing, the system achieves high measurement precision without requiring expensive carrier-phase enhancement GPS equipment, thus resolving the contradiction between accuracy and device complexity
Solution Approach 2:
The patent makes existing sensors serve multiple functions: odometry sensors provide both velocity measurement and trajectory information, GPS provides location data, and radar sensors traditionally used for collision avoidance are repurposed to generate occupancy grids for pose correction. This multi-functionality improves pose accuracy without adding specialized expensive equipment
2Device complexity
If odometry- and GPS-based equipment is used for vehicle pose determination, then device complexity is kept low, but measurement precision deteriorates due to error accumulation
Solution Approach 1:
The patent implements a feedback mechanism where occupancy grids generated from radar sensor data are continuously compared with expected occupancy patterns. The discrepancies detected through this feedback loop generate correction data that is applied to the vehicle pose estimates, thereby compensating for error accumulation from odometry and GPS while maintaining low device complexity
Solution Approach 2:
The patent introduces occupancy grids as an intermediary representation that mediates between raw sensor data and final pose estimates. The occupancy grids serve as a common framework for fusing information from multiple sensors and for generating correction data, improving measurement precision without significantly increasing device complexity
Data Source
AI summary
A vehicle pose determining system and method for accurately estimating the pose of a vehicle (i.e., the location and/or orientation of a vehicle). The system and method use a form of sensor fusion, where output from vehicle dynamics sensors (e.g., accelerometers, gyroscopes, encoders, etc.) is used with output from vehicle radar sensors to improve the accuracy of the vehicle pose data. Uncorrected vehicle pose data derived from dynamics sensor data is compensated with correction data that is derived from occupancy grids that are based on radar sensor data. The occupancy grids, which are 2D or 3D mathematical objects that are somewhat like radar-based maps, must correspond to the same geographic location. The system and method use mathematical techniques (e.g., cost functions) to rotate and shift multiple occupancy grids until a best fit solution is determined, and the best fit solution is then used to derive the correction data that, in turn, improves the accuracy of the vehicle pose data.