Camera-LiDAR Object Annotation for Autonomous Vehicle Maps
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently and accurately annotating objects in map data for navigation, as existing methods are costly, time-consuming, and prone to human error, especially in complex environments.
Innovation Solution
A computer system that fuses LiDAR data points with image data to detect and register target objects, providing additional pose data for enhanced object detection and annotation in map data used for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation methods are used for objects in map data, then annotation can be performed, but the process is costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical annotation processes with an automated computer-implemented system that uses sensor data fusion, machine learning models, and automated object detection algorithms to perform annotation tasks, thereby eliminating the need for manual human intervention and significantly improving productivity
Solution Approach 2:
The system enables self-service annotation by automatically detecting objects in sensor data, generating annotations, and updating map data without requiring human operators, allowing the annotation process to serve itself through automated computational methods
2Reliability
If manual annotation methods are used for objects in map data, then annotation can be performed, but human error is prone
Solution Approach 1:
The patent replaces manual human annotation processes with automated computer-based systems that use sensor data fusion and machine learning algorithms, eliminating human error entirely by substituting mechanical human operations with reliable computational processes
Solution Approach 2:
The system incorporates feedback mechanisms where detected objects are validated through multiple sensor modalities and machine learning confidence scores, allowing the system to self-correct and verify annotations before finalizing them in map data, thereby improving reliability
3Measurement precision
If LiDAR data points are projected onto image data for object detection, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges LiDAR point cloud data with image data into a unified coordinate system, combining the depth information from LiDAR with the visual information from images to create a fused representation that improves detection accuracy while managing complexity through integrated processing
4Measurement precision
If automated object detection is performed using fused sensor data, then annotation accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by processing only the most relevant sensor data and focusing computational resources on detecting only those objects that meet specific criteria, rather than performing exhaustive analysis on all data, thereby reducing energy consumption while maintaining high accuracy for critical detections
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
Improves the accuracy and efficiency of object detection and annotation, providing detailed information for navigating autonomous vehicles in complex environments, reducing human error and operational costs.
Implementation Method 1
obtain LiDAR data points for an environment around an autonomous vehicle
Data Source
AI summary
The present disclosure is directed to a computer system and techniques for automatically annotating objects in map data used for navigating an autonomous vehicle. Generally, the computer system is configured to obtain LiDAR data points for an environment around an autonomous vehicle, project the LiDAR data points onto image data, detect a target object in the image data, extract a subset of the LiDAR data points that corresponds to the detected target object, register the detected target object in map data if the extracted subset of the LiDAR data points satisfies registration criteria, and navigate the autonomous vehicle in the environment according to the map data.