Robotic 3D Pose Estimation Using Simulated Occlusion-Robust Training
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Solution Overview
Problem
Acquiring and labeling real-world data for 3D pose estimation in robotics is challenging, time-consuming, and prone to error, especially for specialized scenarios.
Innovation Solution
Training an autoencoder using simulation data extracted from a simulated environment configured to represent the deployment environment, allowing the autoencoder to predict 3D pose labels without requiring labeled real-world data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems use trained augmented autoencoders with codebook generation and lookup for 3D pose estimation, then labeled training data requirements are reduced, but computational overhead increases and accuracy decreases in specialized scenarios
Solution Approach 1:
The patent creates synthetic training data by rendering 3D models in simulated environments, producing synthetic images with associated ground truth 3D pose labels. This copying approach replaces the need for expensive real-world labeled data while avoiding the computational overhead of codebook generation and lookup, directly resolving the contradiction between reducing data requirements and maintaining system simplicity
Solution Approach 2:
The patent replaces the conventional autoencoder architecture with codebook generation and lookup mechanisms with a direct regression-based neural network approach. The network learns to map image features directly to 3D pose parameters through supervised training on synthetic data, eliminating the complex two-stage process while improving both computational efficiency and accuracy for specialized scenarios
2Reliability
If augmented autoencoders are trained with various noise augmentations to enhance robustness, then noise robustness improves, but the number of parameters required increases significantly
Solution Approach 1:
The patent incorporates noise and occlusion conditions directly into the synthetic training data generation process before training begins. By pre-rendering images with various noise types, lighting conditions, and occlusions at controlled levels, the model learns robust features without requiring complex parameter-heavy architectures, resolving the contradiction between robustness and model simplicity
Solution Approach 2:
The patent uses data augmentation techniques that modify training data parameters (noise levels, occlusion patterns, lighting conditions) rather than modifying the model architecture parameters. This approach achieves noise robustness through input variation during training while keeping the model parameter count manageable, directly addressing the contradiction
3Measurement precision
If real-world data is acquired and labeled for specialized robotics scenarios, then estimation accuracy for specialized scenarios improves, but time consumption and error probability increase
Solution Approach 1:
The patent creates synthetic copies of real-world scenarios through 3D model rendering in simulated environments. By copying the essential geometric and appearance properties of real objects and environments into synthetic data, the system achieves specialized scenario accuracy without the time-consuming process of real-world data collection and labeling
Solution Approach 2:
The patent performs all data generation and labeling operations in advance through automated synthetic data generation pipelines. The 3D models and simulated environments are prepared beforehand, and ground truth labels are automatically generated during rendering, eliminating the time-consuming manual labeling process while maintaining specialized scenario accuracy
Data Source
AI summary
An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.