Multi-Sensor Ground Truth Annotation for Accurate 3D Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for generating ground truth data for deep neural networks in autonomous driving face challenges in accurately labeling 3D objects due to sparse data from LiDAR and RADAR, leading to errors and inefficiencies in training, especially in distinguishing objects like pedestrians and bicycles from other environmental features.
Innovation Solution
An annotation pipeline that synchronizes and aligns data from multiple sensors, decomposes tasks into modular steps, and provides contextual information across sensor modalities to improve labeling accuracy and efficiency, enabling the generation of high-quality 2D and 3D ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional separate labeling processes are used for different sensor modalities, then the labeling workflow is simple, but the labeling accuracy deteriorates due to sparse data and lack of contextual information
Solution Approach 1:
The patent merges multiple separate labeling processes for different sensor modalities (LiDAR, RADAR, cameras) into a unified labeling workflow. The system simultaneously processes and correlates data from multiple sensors to generate ground truth labels, allowing labelers to view and label objects across different sensor types in coordination rather than in isolation, thereby improving labeling accuracy without requiring overly complex separate processes
Solution Approach 2:
The patent introduces a data correlation and synchronization system as an intermediary between raw sensor data and the labeling interface. This intermediary component aligns temporal and spatial data from multiple sensors, provides contextual information to labelers, and manages the complexity of multi-sensor integration behind the scenes, allowing simple labeling operations to achieve high accuracy
2Productivity
If conventional separate labeling processes are used for different sensor modalities, then the process is easy to implement, but the productivity deteriorates due to errors requiring quality checks and rework
Solution Approach 1:
The patent combines multiple labeling processes into a unified workflow that processes LiDAR, RADAR, and camera data simultaneously. This integrated approach allows errors to be detected and corrected in real-time across sensor modalities, reducing rework and improving throughput while maintaining implementation feasibility through a coordinated rather than completely separate process architecture
Solution Approach 2:
The patent implements feedback mechanisms where labeling results from one sensor modality inform and validate labeling in other modalities. The system provides real-time feedback on labeling consistency across sensors, allowing immediate correction of errors before they propagate, thereby improving productivity without requiring complete process redesign
3Loss of information
If conventional separate labeling processes are used, then computational resources are minimized, but the loss of information increases due to sparse data from individual sensors
Solution Approach 1:
The patent performs preliminary data correlation, synchronization, and fusion operations before the actual labeling process. By pre-processing and organizing multi-sensor data with contextual relationships established in advance, the system reduces information loss during labeling while avoiding the need for intensive computational resources during the labeling operation itself, as the heavy integration work is done beforehand
Data Source
AI summary
An annotation pipeline may be used to produce 2D and/or 3D ground truth data for deep neural networks, such as autonomous or semi-autonomous vehicle perception networks. Initially, sensor data may be captured with different types of sensors and synchronized to align frames of sensor data that represent a similar world state. The aligned frames may be sampled and packaged into a sequence of annotation scenes to be annotated. An annotation project may be decomposed into modular tasks and encoded into a labeling tool, which assigns tasks to labelers and arranges the order of inputs using a wizard that steps through the tasks. During the tasks, each type of sensor data in an annotation scene may be simultaneously presented, and information may be projected across sensor modalities to provide useful contextual information. After all annotation tasks have been completed, the resulting ground truth data may be exported in any suitable format.