LiDAR Image Annotation for Static Object Training Data
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Solution Overview
Problem
Existing methods for generating training data for object detection systems in autonomous vehicles, particularly for static objects, are cumbersome, inefficient, and prone to errors due to manual labeling, which is time-consuming and inaccurate.
Innovation Solution
An object manager system that identifies lidar points associated with static objects using sparsely annotated two-dimensional image datasets and projects these points onto non-annotated images to automate the annotation process, leveraging lidar data and image data from multiple sources to enhance object detection training data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling methods are used to generate training data, then annotation accuracy can be maintained through human review, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by using sparsely annotated images to identify static objects and generate candidate training data before full annotation is needed. This preliminary data generation reduces the overall annotation workload while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The patent introduces an intermediary automated system that bridges manual annotation and final training data generation. The system uses sparsely annotated images as intermediaries to guide the identification and segmentation of static objects, reducing direct manual labeling requirements while preserving accuracy.
2Reliability
If manual annotation is performed to ensure data quality, then annotation accuracy improves, but the complexity and time required for the process increases
Solution Approach 1:
The patent segments the annotation process into distinct phases: identifying static objects in sparsely annotated images, generating candidate training data, and verifying results. This segmentation reduces overall process complexity by breaking down the complex manual annotation task into manageable automated and manual steps.
Solution Approach 2:
The system enables self-service by allowing sparsely annotated images to automatically guide the identification and segmentation of static objects. The automated processes serve themselves by using the sparse annotations as references, reducing the need for extensive manual intervention while maintaining data quality.
3Loss of information
If comprehensive manual labeling is performed to capture all static objects, then training data completeness improves, but the time and resources required increase significantly
Solution Approach 1:
The patent applies partial action by using sparsely annotated images rather than fully annotating all images. This partial annotation approach is sufficient to identify static objects and generate comprehensive training data, avoiding the excessive time investment of complete manual labeling while maintaining data completeness.
Solution Approach 2:
The system creates copies of training data by identifying static objects in sparsely annotated images and generating corresponding training examples. This copying approach allows comprehensive training data generation from limited annotated sources, reducing annotation time while maintaining completeness through the propagation of identified object characteristics.
Data Source
AI summary
Techniques for identifying lidar points associated with static objects, and using such lidar points to annotate objects within two-dimensional images are discussed herein. In some examples, an object manager may receive accumulations of lidar data captured from lidar devices of a vehicle while traversing within a driving environment. In some examples, the object manager may receive a plurality of annotated images. Such annotations may identify static objects within the driving environment. In some instances, the object manager may project a lidar point into an annotated image and determine that the lidar point is associated with an annotated pixel. Based on the pixel being associated with the annotated object, the object manager may determine that the lidar point is associated with object. In some examples, the object manager may determine a subset of lidar points that are associated with the object.