Multi-Modal Point Cloud Segmentation for Accurate Semantic Mapping
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Solution Overview
Problem
Current autonomous vehicle systems face challenges in generating accurate and efficient semantic maps due to the limitations of using single sensor modalities, such as LiDAR, which can result in incomplete or inaccurate object classification and mapping.
Innovation Solution
A multi-modal segmentation network that combines data from LiDAR sensors and cameras to predict semantic labels, integrating raw point features from LiDAR with rich point features generated from enhanced camera images, thereby enhancing the accuracy and efficiency of map generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only LiDAR sensor data is used for semantic labeling, then the system complexity is reduced, but the accuracy and completeness of object classification deteriorates
Solution Approach 1:
The patent combines data from multiple sensor modalities (LiDAR and cameras) into a unified processing framework. The LiDAR point cloud data and camera image data are integrated through feature extraction and fusion algorithms, allowing the system to leverage the complementary strengths of each sensor type for more accurate semantic labeling while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a composite information representation by fusing features from different sensor sources. The semantic labeling system processes combined features from LiDAR (geometric and spatial information) and camera (textural and color information) sensors, producing a composite semantic map that is more accurate than what either sensor could provide alone.
2Measurement precision
If multi-modal sensor data is integrated, then the accuracy of semantic labeling is improved, but the device complexity increases
Solution Approach 1:
The patent divides the complex multi-modal processing task into distinct modular components: LiDAR data processing module, camera image processing module, feature fusion module, and semantic labeling module. Each module handles specific aspects of the data flow independently, making the overall complex system more manageable through functional segmentation and reducing inter-module dependencies.
Solution Approach 2:
The patent introduces intermediary processing layers including feature extraction modules and coordinate transformation modules that mediate between raw sensor data and the final semantic labeling. These intermediaries standardize data formats and features before fusion, simplifying the integration process and reducing the complexity of direct multi-modal data combination.
3Reliability
If rich feature extraction from camera images is performed, then the quality of semantic labels is enhanced, but the processing time and computational load increase
Solution Approach 1:
The patent applies partial feature extraction by selecting only the most relevant features from camera images for semantic labeling tasks. Rather than processing all possible image features, the system extracts specific features (such as color histograms, edge detections, or texture descriptors) that are most useful for distinguishing semantic categories, reducing computational load while maintaining label quality.
Solution Approach 2:
The patent performs preliminary processing of camera images including preprocessing steps (resizing, normalization, noise reduction) and early feature extraction before the main semantic labeling process. This preliminary action prepares the data in advance, reducing the computational burden during real-time processing and accelerating overall map generation while preserving essential information for accurate labeling.
Data Source
AI summary
Provided are methods for enhanced semantic labeling in mapping with a semantic labeling system, which can include receiving, from a LiDAR sensor of a vehicle, LiDAR point cloud information including at least one raw point feature for a point, receiving, from a camera of the vehicle, image data associated with an image captured using the camera, generating at least one rich point feature for the point based on the image data, predicting, using a LiDAR segmentation neural network and based on the at least one raw point feature and the at least one rich point feature, a point-level semantic label for the point, and providing the point-level semantic label to a mapping engine to generate a map based on the point-level semantic label Systems and computer program products are also provided.