Robot Traversability Estimation Using Uncertainty-Aware Synthetic Point Clouds
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Solution Overview
Problem
Conventional traversability estimation methods for robotic devices face challenges in handling uncertainties and inaccuracies due to sensor noise, occlusions, and dynamic changes in real-world environments, particularly in rough-terrain applications, and the generation of high-quality ground-truth labels for training neural networks is a significant challenge.
Innovation Solution
A machine learning-based system that utilizes optimum-fidelity scan data to generate elevation maps, dense point clouds, and synthetic point clouds, predicting traversability features with uncertainty estimation using a neural network model like UNRealNet, which adapts to real-world scenarios through training and re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision scanning technologies are used to gather detailed environmental data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses neural networks to generate synthetic point cloud data that copies and simulates the appearance and structure of real environmental data. This allows the system to train on realistic-looking synthetic data instead of requiring expensive high-precision scanning equipment for all training scenarios, thereby reducing device complexity while maintaining measurement precision for navigation purposes.
Solution Approach 2:
The patent introduces an intermediary neural network model that translates low-resolution sensor data into high-quality traversability predictions. This intermediary layer allows the system to use simpler, lower-cost sensors while achieving the performance of high-precision scanning through the neural network's ability to infer detailed environmental characteristics from incomplete data.
2Adaptability or versatility
If deep neural networks are used to generate complex forecasts capturing abstract environmental priors, then adaptability to real-world scenarios is improved, but difficulty of detecting and measuring (training data requirements) increases
Solution Approach 1:
The patent generates synthetic point cloud data with ground-truth labels by copying and transforming real environmental data through neural networks. This synthetic data contains accurate ground-truth information about traversability, allowing the neural network to be trained on realistic scenarios without requiring manual annotation or complex measurement processes for each training sample.
Solution Approach 2:
The patent performs preliminary action by pre-generating and pre-labeling synthetic training data before the actual navigation task. The neural network is trained in advance on synthetic environments that capture a wide range of possible scenarios, so that during actual navigation, the system can quickly apply its learned knowledge without needing to measure or detect ground-truth labels in real-time.
3Quantity of substance
If simulation is used to generate labeled data, then quantity of substance (training data volume) is improved, but measurement precision and realism of environmental representation deteriorates
Solution Approach 1:
The patent uses neural networks to copy and transform real environmental data into synthetic data that preserves the essential characteristics and accuracy of real environments. The synthetic point clouds are generated by copying the structural and visual properties of real scans, ensuring that the training data maintains high measurement precision while providing sufficient volume through automated generation.
Solution Approach 2:
The patent applies parameter changes by transforming real environmental data through neural network mappings that preserve geometric and topological relationships while generating varied synthetic scenarios. This allows the system to maintain the precision of real-world measurements while generating large quantities of diverse training data by parameterizing different environmental conditions.
Data Source
AI summary
A ML-based system and method for determining traversability with uncertainty object estimation for one or more robot devices to navigate through one or more terrains, is disclosed. The ML-based method comprises: (a) obtaining optimum-fidelity scan data in a form of point cloud from scanner devices; (b) generating an elevation map of the environments by applying an elevation mapping model and free-space detection model on the point cloud; (c) generating a dense point cloud with ground-truth map features from the elevation map of the environments; (d) generating a synthetic point cloud based on the dense point cloud of the environments; (e) predicting traversability features from the synthetic point cloud associated with the environments using a ML model; and (f) determining the traversability with the uncertainty object estimation, which adapts the robot devices to navigate on the terrains, based on the traversability features predicted from the ML model.