LiDAR Cost Volume Inference for Accurate Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LiDAR localization methods for autonomous vehicles require substantial engineering efforts and are scenario-dependent, lacking in effectively utilizing neural networks for 3D geometry-related tasks such as precise localization.
Innovation Solution
A computer-implemented method using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to construct and regularize a cost volume for solution inference, enabling the extraction of optimal pose offsets for autonomous vehicle localization by matching online LiDAR point clouds with pre-built point cloud maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing LiDAR localization methods are used, then localization accuracy can be achieved, but substantial engineering efforts and scenario-specific tuning are required
Solution Approach 1:
The patent replaces traditional handcrafted geometric algorithms (mechanical/system-based approach) with deep learning neural networks that automatically learn localization features from data. This substitution eliminates the need for manual engineering efforts and scenario-specific tuning while maintaining high localization accuracy through end-to-end learned representations.
Solution Approach 2:
The patent transforms the localization problem from a geometric parameter-matching task to a learned parameter estimation task. By changing the approach from explicit geometric computations to neural network parameter optimization, the system achieves accuracy without requiring manual engineering of geometric relationships.
2Adaptability or versatility
If traditional neural networks are used for semantics, then good semantic results are achieved, but they lack effectiveness in solving 3D geometry tasks like localization
Solution Approach 1:
The patent merges semantic understanding capabilities with 3D geometric reasoning by integrating neural network-based semantic feature extraction with spatial transformation and pose estimation modules. This combination allows the system to simultaneously leverage semantic information and geometric constraints for robust localization.
Solution Approach 2:
The patent creates a composite architecture that combines different neural network components (semantic processing layers, geometric transformation layers, and regression layers) to handle both semantic and geometric aspects of localization. This composite structure enables the system to process semantic information while reliably solving 3D geometry tasks.
3Measurement precision
If handcrafted localization systems are used, then high accuracy is achieved, but they require substantial human labeling and engineering effort
Solution Approach 1:
The patent enables the system to automatically learn localization features and parameters directly from raw LiDAR point cloud data without requiring manual labeling or handcrafted feature engineering. The neural network performs self-service by automatically adapting to different scenarios and learning optimal feature representations from unlabeled or minimally labeled data.
Solution Approach 2:
The patent performs preliminary learning during the training phase where the neural network automatically acquires localization knowledge from training data. This preliminary action eliminates the need for subsequent manual engineering and labeling efforts when deploying the system to new scenarios.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
A method for solution inference using neural networks in LiDAR localization includes constructing a cost volume in a solution space for a predicted pose of an autonomous driving vehicle (ADV). The cost volume includes a number of sub volumes. Each sub volume represents a matching cost between a keypoint from an online point cloud and a corresponding keypoint on a pre-built point cloud map (1001). The method further includes regularizing the cost volume using convention neural networks (CNNs) to refine the matching costs (1003); and inferring, from the regularized cost volume, an optimal offset of the predicted pose. The optimal offset can be used to determine a location of the ADV (1005).