Autonomous Vehicle Map Data Creation Using Relative Positioning
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Solution Overview
Problem
Autonomous movement systems face challenges in generating map data in outdoor environments lacking distinguishable objects for shape matching, leading to potential position identification errors and incomplete map creation due to reliance on odometry, IMU, and GNSS limitations.
Innovation Solution
A system comprising multiple autonomous vehicles that communicate to measure and share position and object shape data, using one vehicle as a reference to calculate relative positions and update map data, even in areas with few recognizable shapes, by employing laser scanning and camera technology for accurate shape measurement and self-localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shape matching is used for autonomous movement and map data creation, then positioning accuracy is improved in environments with distinguishable objects, but map data cannot be created in zones lacking distinguishable objects for matching
Solution Approach 1:
The system divides the environment into multiple zones based on the presence or absence of distinguishable objects. In zones with distinguishable objects, shape matching is used for high-precision positioning and map creation. In zones lacking distinguishable objects, the system switches to alternative methods (odometry, IMU, GNSS) to ensure continuous map data creation capability across all environments.
Solution Approach 2:
The autonomous movement apparatus is equipped with multiple positioning and measurement functions: shape matching for environments with distinguishable objects, odometry and IMU for general movement tracking, and GNSS for outdoor positioning. This multi-functional capability allows the system to adapt to various environments and maintain both positioning accuracy and map data creation capability regardless of environmental conditions.
2Adaptability or versatility
If odometry and IMU are used to assist position identification in zones without distinguishable objects, then map data creation is enabled, but measurement errors may be increased and accumulated with movement
Solution Approach 1:
The system continuously monitors positioning data from multiple sources (shape matching, odometry, IMU, GNSS) and uses feedback mechanisms to detect error accumulation. When error accumulation is detected in odometry or IMU measurements, the system adjusts its positioning strategy by relying more heavily on other measurement sources or by performing recalibration, thereby maintaining overall positioning accuracy while preserving map data creation capability.
Solution Approach 2:
The positioning system uses a composite approach by combining multiple measurement methods (shape matching, odometry, IMU, GNSS) into an integrated positioning solution. Each method compensates for the weaknesses of others: shape matching provides high accuracy when available, odometry and IMU provide continuous tracking capability, and GNSS provides absolute positioning references. This composite measurement system maintains both adaptability and precision.
3Measurement precision
If GNSS is used for position measurement in outdoor environments, then positioning capability is provided, but map data generation becomes difficult depending on locations due to satellite visibility limitations
Solution Approach 1:
The system uses shape matching as an intermediary method between GNSS and the final map data creation process. When GNSS provides positioning data in outdoor environments, shape matching serves as an intermediary to refine the position information and enable map data generation even in areas with limited satellite visibility. This intermediary approach allows the system to overcome GNSS limitations while maintaining positioning capability.
Solution Approach 2:
The system performs preliminary positioning using available methods (GNSS when visible, odometry, IMU) before attempting map data creation. By establishing preliminary position information in advance, the system can then use shape matching or other available measurement methods to complete the map data generation process even in locations where GNSS satellite visibility is limited, thereby ensuring continuous map data creation capability.
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 the generation of highly accurate map data and self-localization in environments with limited recognizable features, reducing position identification errors and improving navigation accuracy.
Implementation Method 1
object shape data of a peripheral object which is measured by a measurement device (internal sensor and/or external sensor) installed in the moving object
Implementation Method 2
an autonomous movement system is disclosed which estimates a self-position and moves according to a target path, with reference to map data responding to real environment
Data Source
AI summary
The invention is intended to be able to generate map data even if a location not suited for identification of a matching position exists. A map data creation device creates map data, the map data being used for autonomous movement by a vehicle (1a), a vehicle (1b) and/or other vehicles (1), based on a relative position of the vehicle (1b) which exists around the vehicle (1a), relative to the vehicle (1a), the relative position being measured by the vehicle (1a), and object shape data of an object which exists around the vehicle (1b), the object shape being measured by the vehicle (1b). Moreover, the relative position of the vehicle (1b) relative to the vehicle (1a) is calculated based on the object shape data measured by the vehicle (1a).