Vehicle Position Estimation Using Road-Surface LiDAR Motion Data
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Solution Overview
Problem
Existing position estimation systems for moving bodies, such as vehicles, face accuracy issues when there are no characteristic objects in the surroundings, leading to decreased estimation accuracy due to the inability to acquire displacement amounts between feature points.
Innovation Solution
A position estimation apparatus that uses a detection unit to irradiate surroundings with electromagnetic waves, acquiring point cloud data including position and speed information, extracts road surface data, and calculates translational and rotational motion components to update the vehicle's position accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point displacement method is used for position estimation, then position estimation can be performed using visual field images, but estimation accuracy decreases when there are no characteristic objects in the surroundings
Solution Approach 1:
The patent introduces an intermediary coordinate transformation system that bridges the gap between image coordinates and real-world position coordinates. By using multiple coordinate systems (image coordinates, sensor coordinates, vehicle coordinates, and global coordinates) and transformation matrices, the system can accurately estimate position even when traditional feature point methods fail due to lack of characteristic objects in the environment.
Solution Approach 2:
The patent replaces the mechanical/visual feature point tracking system with an electromagnetic wave-based detection system (LiDAR). By using laser ranging to directly measure distances to surrounding objects and constructing point cloud data, the system obtains position information without relying on visual feature points, thereby maintaining accuracy in environments with few characteristic objects.
2Measurement precision
If electromagnetic wave detection is used to acquire position information, then position can be estimated regardless of surrounding features, but device complexity increases
Solution Approach 1:
The patent makes the detection unit (LiDAR) perform multiple functions: it not only measures distances to objects for position estimation but also generates point cloud data that can be used for environmental mapping, obstacle detection, and motion analysis. This multi-functionality justifies the added device complexity by providing multiple benefits from a single sensor system.
Solution Approach 2:
The patent changes the measurement parameters from 2D image coordinates to 3D spatial coordinates with distance information. By using laser ranging to obtain precise distance measurements and constructing point cloud data with three-dimensional position information, the system achieves higher measurement precision that compensates for the increased device complexity.
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 self-position estimation of vehicles regardless of their surroundings by utilizing electromagnetic wave detection and motion component calculations, enhancing accuracy even in environments lacking distinct features.
Implementation Method 1
a detection unit mounted on a moving body and configured to irradiate surroundings of the moving body with an electromagnetic wave to detect an external environment situation in the surroundings based on a reflected wave
Implementation Method 2
point cloud data including position information of a measurement point on a surface of an object from which the reflected wave is obtained and speed information indicating a relative moving speed of the measurement point
Data Source
AI summary
A position estimation apparatus includes: a detection unit mounted on a moving body and irradiating surroundings of the moving body with an electromagnetic wave; and a microprocessor. The microprocessor is configured to perform acquiring point cloud data, the point cloud data including position information of a measurement point on a surface of an object from which a reflected wave of the electromagnetic wave is obtained and speed information indicating a relative moving speed of the measurement point; extracting road surface point cloud data corresponding to a road surface from the point cloud data; calculating a translational motion component and a rotational motion component, based on the position information and the speed information of the measurement point corresponding to the road surface point cloud data; and updating position information indicating a position of the moving body, based on the translational motion component and the rotational motion component.