Autonomous Vehicle LiDAR-Camera Fusion for Point Cloud Labeling
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Solution Overview
Problem
Existing autonomous driving systems face challenges in effectively combining and analyzing point cloud data from multiple LiDAR sensors to accurately detect and characterize objects in the environment for safe navigation.
Innovation Solution
A method involving signal processing techniques that combine point cloud data from multiple LiDAR sensors, project data onto a common coordinate system, and integrate with camera data to assign labels and bounding boxes, enabling precise object detection and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data from multiple LiDAR sensors is combined and processed, then object detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the point cloud data processing by dividing it into multiple stages: initial data collection from multiple LiDAR sensors, preliminary filtering and processing of individual sensor data, then combining processed data sets. This segmentation reduces the computational burden on any single processing stage while maintaining overall detection accuracy.
Solution Approach 2:
The patent applies preliminary filtering and processing to point cloud data from each LiDAR sensor before combining them. By pre-processing individual data sets to remove noise and extract relevant features beforehand, the system reduces the complexity of subsequent combined processing while preserving detection accuracy.
2Area of stationary object
If multiple LiDAR sensors are used to scan areas, then coverage and detection capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent combines point cloud data from multiple LiDAR sensors positioned at different locations on the autonomous vehicle. By merging these data sets into a unified point cloud representation, the system achieves comprehensive environmental coverage while managing complexity through integrated processing rather than handling each sensor independently.
Solution Approach 2:
The patent implements a universal signal processing framework that handles data from multiple LiDAR sensors with different scanning patterns and coverage areas. This multi-functional approach allows the same processing pipeline to effectively manage diverse sensor inputs, reducing overall system 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
Enhances the accuracy of object detection and characterization, allowing for safer and more precise autonomous vehicle operations by integrating data from multiple LiDARs and cameras.
Implementation Method 1
Light Detection and Ranging (LiDAR)
Data Source
AI summary
Autonomous vehicles can include systems and apparatus for performing signal processing on point cloud data from Light Detection and Ranging (LiDAR) devices located on the autonomous vehicles. A method includes obtaining, by a computer located in an autonomous vehicle, a combined point cloud data that describes a plurality of areas of an environment in which the autonomous vehicle is operating; determining that a first set of points from the combined point cloud data are located within fields of view of cameras located on the autonomous vehicle; assigning one or more labels to a second set of points from the first set of points in response to determining that the second set of points are located within bounding box(es) around object(s) in images obtained from the cameras; and causing the autonomous vehicle to operate based on characteristic(s) of the object(s) determined from the second set of points.