Vehicle Position Estimation Using LIDAR Landmark Map Correction
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Solution Overview
Problem
Conventional methods for estimating a vehicle's current position in autonomous driving systems are insufficient in accuracy, as they rely mainly on internal sensors and do not provide a method for calculating the absolute position with high precision.
Innovation Solution
An estimation device that acquires map information and first information indicating distance and angle to objects within a range, using this data to estimate the position of a moving body based on position information from the map, incorporating a second estimation unit to calculate a current position by correcting previous estimates with differences in positional relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods using internal sensors are used for position estimation, then the system is simple to operate, but the measurement precision is insufficient
Solution Approach 1:
The patent introduces map information as an intermediary element between the vehicle's internal sensors and the final position estimation. The map database stores pre-acquired position information of landmarks, which serves as a reference framework. The LIDAR detects objects in the real environment, and by matching these detections with the map database, the system achieves high-precision position estimation without directly relying on error-prone internal sensors alone.
Solution Approach 2:
The patent replaces reliance on mechanical/internal sensors (which have accumulation errors) with an optical measurement system (LIDAR) that measures distance and angle to landmarks. This substitution uses light-based measurement to determine position relative to the map database, achieving higher precision by replacing the mechanical sensor-based approach with an optical ranging approach.
2Measurement precision
If LIDAR scanning is performed to detect objects for position estimation, then the measurement precision improves, but the productivity decreases due to increased computational burden
Solution Approach 1:
The map database is prepared in advance by acquiring and storing position information of landmarks during map construction phases. This preliminary action creates a ready-to-use reference framework that eliminates the need for real-time processing of all environmental features, allowing the system to quickly match LIDAR detections against pre-processed map data during actual position estimation.
Solution Approach 2:
The system extracts only the essential features (landmarks with their position information) from the complete environmental data and stores them in the map database. During operation, only these extracted landmark features are matched against LIDAR detections, rather than processing all detected objects, thereby reducing computational burden while maintaining precision.
3Measurement precision
If multiple estimation units are used to improve accuracy under different conditions, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent designs the estimation device with a universal architecture where the core estimation unit can handle multiple estimation scenarios by switching between different input data combinations. The same basic estimation mechanism processes both cases: when both LIDAR object detection and map information are available, and when only map information is available. This multi-functionality allows the system to adapt to different operational conditions without requiring completely separate estimation systems.
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
Enables precise estimation of a moving body's position using registered feature information on maps, improving accuracy and reducing computational burden, and can function effectively in various conditions such as nighttime or when road markings are obscured by snow.
Implementation Method 1
a LIDAR which detects a point group of a surface of an object by performing horizontal scanning with intermittently-emitted laser light and receiving the reflective light (scattering light)
Data Source
AI summary
Provided is an estimation device capable of estimating a current position with a high degree of accuracy. A driving support system includes: a vehicle mounted device 1 that is mounted on a vehicle and performs a control concerning vehicle driving support; a LIDAR 2; a gyroscope sensor 3; and a vehicle speed sensor 4. The vehicle mounted device 1 acquires, from the LIDAR 2, a measurement value ztk indicating a positional relationship between a reference landmark Lk of an index k and the own vehicle. The vehicle mounted device 1 calculates a post estimated value x{circumflex over ( )}t by correcting a prior estimated value x t estimated, on the basis of the measurement value ztk and a position vector mk of the reference landmark Lk included in a map DB 10, from a moving speed measured by the vehicle speed sensor 4 and an angular rate measured by the gyroscope sensor 3.