3D CNN LiDAR Localization for Centimeter-Level Pose Inference
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Solution Overview
Problem
Existing LiDAR localization methods for autonomous vehicles require substantial engineering efforts and are scenario-dependent, lacking in accurately estimating 3D geometry and position with centimeter-level precision without extensive hardcoding and tuning.
Innovation Solution
A learning-based LiDAR localization system using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) constructs a cost volume for solution inference, refining matching costs to determine an optimal pose of the autonomous vehicle by comparing online point clouds with pre-built maps, minimizing human labeling efforts and achieving high localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing LiDAR localization approaches use multiple stages in a localization pipeline with handcoding and tuning, then localization accuracy and robustness are improved, but engineering complexity and scenario dependency increase
Solution Approach 1:
The patent replaces the mechanical/engineering-based localization pipeline (handcoding, tuning, multiple processing stages) with a neural network-based system. The CNN processes point cloud data end-to-end to directly output localization results, eliminating the need for manual feature engineering and multi-stage processing while maintaining centimeter-level accuracy.
Solution Approach 2:
The patent changes the fundamental approach from deterministic handcrafted algorithms to learned probabilistic models. By training the neural network on diverse scenario data, the system adapts to various environments without manual tuning, reducing engineering complexity while maintaining robustness across different scenarios.
2Measurement precision
If existing LiDAR localization approaches use handcoding and tuning, then localization accuracy is improved, but adaptability to different scenarios deteriorates
Solution Approach 1:
The patent creates a universal localization system using neural networks that can handle multiple scenarios without scenario-specific tuning. The CNN is trained on diverse data including different environments, weather conditions, and road types, enabling it to generalize across scenarios while maintaining high accuracy, thus achieving both precision and adaptability.
3Measurement precision
If neural networks are used for semantics processing, then processing results are improved, but 3D geometry solving capability deteriorates
Solution Approach 1:
The patent merges semantics processing and 3D geometry solving into a single unified neural network framework. The CNN simultaneously processes semantic information from point clouds and performs 3D localization tasks by learning features that capture both semantic context and geometric relationships, achieving high precision in both domains without the limitations of separate processing pipelines.
Data Source
AI summary
In one embodiment, 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 including a number of sub volumes, each sub volume representing a matching cost between a keypoint from an online point cloud and a corresponding keypoint on a pre-built point cloud map. The method further includes regularizing the cost volume using convention neural networks (CNNs) to refine the matching costs; 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.