3D LIDAR Bounding Box Estimation for Faster Annotation
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Solution Overview
Problem
Manual annotation of 3-D LIDAR point clouds for vehicular driver assist and autonomous driving systems is time-consuming, expensive, and prone to errors, and the automated detection of unusual stationary objects poses data scalability issues.
Innovation Solution
The method provides fast and accurate annotation cluster pre-proposals on a minimally-supervised or unsupervised basis, segments drivable surfaces/ground planes in a bird's-eye-view construct, and generates labels based on feature-based detection of similar objects in already-annotated frames, using automated clustering algorithms and feature extraction techniques like Eigen value-based methods and ensemble shapes, to reduce ambiguity and training time for manual annotators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of 3-D LIDAR point clouds is performed, then accurate annotated data is obtained for training ML algorithms, but the process is time-consuming and expensive
Solution Approach 1:
The system performs preliminary automated annotation using clustering algorithms and feature extraction to generate pre-proposals before manual annotation. This preliminary action provides a head start for annotators, reducing the time needed to achieve accurate annotations while maintaining high precision through automated feature-based detection of similar objects
Solution Approach 2:
The system creates copies of annotation patterns from already-annotated frames and applies them to new data through feature-based detection. By copying structural features and annotation labels from similar previously-detected objects, the system reduces manual annotation time while maintaining consistency and accuracy across the dataset
2Measurement precision
If manual annotation is performed by expert annotators, then high accuracy is achieved, but expertise and expense increase
Solution Approach 1:
The system enables self-service annotation by automatically detecting features, clustering points, and generating pre-proposals without requiring expert annotators for every annotation task. The automated feature extraction and similarity-based labeling allow the system to annotate data independently, reducing both expertise requirements and costs while maintaining accuracy through algorithmic consistency
Solution Approach 2:
The system copies annotation patterns and structural features from already-annotated frames to new data, eliminating the need for expensive expert annotators to manually analyze each object. By detecting similar objects through feature comparison and applying copied labels, the system achieves high accuracy at minimal cost
3Productivity
If automated detection of unusual stationary objects is performed, then data scalability improves, but reasonable doubt and annotation ambiguity increase
Solution Approach 1:
The system implements feedback by comparing detected objects against already-annotated frames and using the results to refine future detections. The feedback loop allows the system to learn from annotated data, improving detection reliability for unusual stationary objects while maintaining scalability through automated feature-based comparison and confidence scoring
Data Source
AI summary
Methods and systems for generating annotated data for training vehicular driver assist (DA) and autonomous driving (AD) active safety (AS) functionalities and the like. More specifically, methods and systems for the fast estimation of three-dimensional (3-D) bounding boxes and drivable surfaces using LIDAR point clouds and the like. These methods and systems provide fast and accurate annotation cluster pre-proposals on a minimally-supervised or unsupervised basis, segment drivable surfaces/ground planes in a bird's-eye-view (BEV) construct, and provide fast and accurate annotation cluster pre-proposal labels based on the feature-based detection of similar objects in already-annotated frames. The methods and systems minimize the expertise, time, and expense associated with the manual annotation of LIDAR point clouds and the like in the generation of annotated data for training machine learning (ML) algorithms and the like.