Point Cloud Annotation via Confidence-Based Sensor Fusion
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Solution Overview
Problem
The inefficiency and high cost of annotating point cloud data due to insufficient data density and angle changes during collection, leading to time-consuming and inaccurate manual annotation processes.
Innovation Solution
A method and apparatus that utilize laser radar and non-laser radar sensors to collect data, segment and track point clouds, recognize and track feature objects, and correct segmentation results based on confidence levels, reducing manual work and annotation costs by determining results with confidence levels above a threshold as annotation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is performed on point cloud data with insufficient density and angle changes, then annotation accuracy deteriorates, but annotation time increases
Solution Approach 1:
The system performs preliminary segmentation and tracking of point cloud data to generate candidate annotation results before manual review, pre-processing the data to identify potential objects and their trajectories, thereby reducing the time required for manual annotation while maintaining accuracy
Solution Approach 2:
The system introduces an automated confidence-based filtering mechanism as an intermediary between raw point cloud data and final annotations, using confidence levels to objectively evaluate and select reliable annotation candidates, improving both accuracy and efficiency
2Productivity
If automated recognition algorithms are used for point cloud annotation, then annotation efficiency improves, but annotation accuracy deteriorates due to insufficient data density
Solution Approach 1:
The system merges multiple data sources including point cloud data, sensor data, and video data to compensate for insufficient density in any single source, creating a more robust basis for automated recognition while maintaining high annotation efficiency
Solution Approach 2:
The system implements a feedback mechanism where confidence levels of automated recognition results are evaluated and used to determine whether manual verification is needed, allowing the system to self-correct and improve accuracy while maintaining efficiency for high-confidence cases
3Reliability
If multiple sensors are used to collect data, then data quality improves, but system complexity increases
Solution Approach 1:
The system uses a unified data processing framework that can handle multiple sensor types (laser radar, non-laser radar, video cameras) through common segmentation and tracking algorithms, allowing multi-functional data collection without proportionally increasing system complexity
Solution Approach 2:
The system extracts only the essential features and confidence level information from multi-sensor data that are necessary for annotation, filtering out redundant information and simplifying the data structure while maintaining data quality and reliability
Data Source
AI summary
The present application discloses a method and apparatus for annotating point cloud data. A specific implementation of the method includes: collecting data in a given scenario by using a laser radar and a non-laser radar sensor to respectively obtain point cloud data and sensor data; segmenting and tracking the point cloud data to obtain point cloud segmentation and tracking results; recognizing and tracking feature objects in the sensor data to obtain feature object recognition and tracking results; correcting the point cloud segmentation and tracking results by using the feature object recognition and tracking results, to obtain confidence levels of the point cloud recognition and tracking results; and determining a point cloud segmentation and tracking result whose confidence level is greater than a confidence level threshold as a point cloud annotation result. This implementation reduces the amount of manual work required for annotating point cloud data, thereby reducing the annotation costs.