Movable Body Control Using Adaptive Multi-LiDAR Point Cloud Estimation
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Solution Overview
Problem
Existing technologies for self-traveling conveyance of movable bodies, such as vehicles, face a trade-off between estimation accuracy and processing speed for position and orientation estimation, with insufficient methods to enhance accuracy while maintaining processing efficiency.
Innovation Solution
A control device that combines three-dimensional point cloud data from multiple distance measurement devices to enhance estimation accuracy, allowing for prioritization of accuracy or speed based on processing time and required accuracy or traveling situation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If combined point cloud data from multiple distance measurement devices is used for estimation, then estimation accuracy for position and orientation is improved, but processing time increases
Solution Approach 1:
The system dynamically adjusts the estimation process by switching between using combined point cloud data from multiple distance measurement devices and using single device data based on real-time evaluation of allowable processing time and required accuracy. This dynamic adaptation resolves the contradiction by making the system flexible rather than fixed in its data processing approach.
Solution Approach 2:
The system changes the parameter of data source selection (single device vs. combined multiple devices) based on the evaluation results of processing time and accuracy requirements. By changing this parameter dynamically, the system optimizes the balance between estimation accuracy and processing speed for different operational scenarios.
2Measurement precision
If combined point cloud data is used to enhance estimation accuracy, then control accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the estimation process into two distinct paths: one using combined point cloud data from multiple devices for high accuracy scenarios, and another using single device data for speed-critical scenarios. This segmentation allows the complex combined processing to be used selectively rather than continuously, managing system complexity while maintaining accuracy where needed.
Solution Approach 2:
The system dynamically selects between different estimation approaches based on real-time conditions, avoiding the need for a permanently complex system that always processes combined data. The complexity is activated only when and where it is needed, based on the evaluation of accuracy requirements and processing time availability.
3Productivity
If single point cloud data is used for estimation, then processing speed is improved, but estimation accuracy deteriorates
Solution Approach 1:
The system applies partial action by using only single device point cloud data when high processing speed is required and combined data is not necessary. Conversely, it applies excessive action (using all available devices) when high accuracy is required and processing time is sufficient. This partial/excessive approach resolves the contradiction by avoiding unnecessary processing overhead when full capability is not needed.
Solution Approach 2:
The system changes the parameter of data source selection between single device and combined multiple devices based on the evaluation of processing speed requirements versus accuracy requirements. This parameter change allows the system to optimize for speed when appropriate while maintaining the option for high accuracy when needed.
Data Source
AI summary
A control device is configured to generate a control command for controlling a movable body, using measurement results of a plurality of distance measurement devices. The control device includes a point cloud combining unit configured to create combined point cloud data by combining two or more pieces of three-dimensional point cloud data that are obtained by two or more distance measurement devices of the plurality of distance measurement devices. The control device is configured to execute a first estimation process of estimating at least one of the position and orientation of the movable body, using the combined point cloud data.