LiDAR Sensor Point Cloud Gap Filling
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Solution Overview
Problem
Existing distance measurement systems often fail to acquire complete distance data in areas not covered by other devices, leading to gaps in three-dimensional point cloud data, which can affect the accuracy and efficiency of environmental sensing and navigation systems.
Innovation Solution
A LiDAR sensor system that includes a light-emitting device, a light-receiving device, and a processing circuit capable of emitting a light beam in various directions, selectively measures distances in empty areas within the point cloud data by referencing stored three-dimensional point cloud data and prioritizing measurements based on importance and availability of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple distance measurement devices are used to cover more areas, then the coverage area is improved, but the device complexity increases
Solution Approach 1:
The patent combines data from multiple distance measurement devices (including cameras and LiDAR sensors) into a unified point cloud dataset. By merging the coverage areas of multiple devices and using their respective strengths, the system achieves comprehensive scene coverage without requiring each individual device to be overly complex. The processing circuit integrates data from different sources to create a complete three-dimensional representation.
Solution Approach 2:
The system employs multiple types of distance measurement devices that serve different functions - cameras capture visual information and depth maps, while LiDAR sensors provide precise distance measurements. Each device type is optimized for its specific function, yet all contribute to the overall goal of comprehensive scene mapping. This multi-functional approach allows the system to achieve wide coverage without increasing the complexity of any single device.
2Measurement precision
If distance measurement is performed in all areas, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The system performs preliminary distance measurements using cameras to generate depth maps before using LiDAR sensors for precise measurements. The camera-based preliminary measurement identifies areas that require more accurate LiDAR measurement, allowing the system to focus computational resources only where needed. This preliminary action reduces the overall measurement time while maintaining precision in critical areas.
Solution Approach 2:
The patent applies different measurement strategies to different areas of the scene based on their importance and the quality of existing data. Areas with sufficient camera depth information may not require LiDAR measurement, while areas requiring high precision (such as objects of interest or regions with poor camera coverage) receive focused LiDAR attention. This local quality approach optimizes the balance between measurement precision and time consumption.
3Productivity
If LiDAR sensor measures distance in empty areas only, then the productivity is improved, but the measurement precision may worsen in already covered areas
Solution Approach 1:
The system uses feedback from the point cloud data to identify empty areas that require LiDAR measurement. The processing circuit continuously monitors the completeness of the point cloud and directs LiDAR measurements to areas with gaps or insufficient data. This feedback mechanism ensures that LiDAR measurements are applied productively only where needed, while maintaining or improving overall measurement precision through targeted supplementation of missing data.
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
This approach enables the acquisition of more comprehensive distance data, enhancing the accuracy and efficiency of point cloud generation and improving the operational capabilities of systems like self-driving vehicles and environmental sensing.
Implementation Method 1
a light-emitting device that can change an emission direction of a light beam, a light-receiving device that detects reflected light from the light beam
Implementation Method 2
generate the distance measurement data on the basis of the signal outputted from the light-receiving device
Data Source
AI summary
A distance measurement system comprises distance measurement devices, including a LiDAR sensor and at least one other distance measurement device, and a storage device storing three-dimensional point cloud data based on distance measurement data acquired by each of the distance measurement devices. The LiDAR sensor comprises a light-emitting device that can change an emission direction of a light beam, a light-receiving device that detects reflected light from the light beam and outputs a signal indicating a detection result, and a processing circuit that controls the light-emitting device and the light-receiving device to generate the distance measurement data on a basis of the signal outputted from the light-receiving device. The processing circuit references the point cloud data to determine at least one empty area in the point cloud data, and measures distance in the empty area by causing the light-emitting device to emit the light beam toward the empty area.