Dense-Depth Camera–LiDAR Fusion for Long-Range Detection
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Solution Overview
Problem
Existing autonomous vehicle perception systems face challenges in achieving high precision and recall for long-range object detection due to the limitations of sparse depth data from LiDAR sensors and misalignment issues in fusing camera and LiDAR sensor data.
Innovation Solution
A perception system that transforms camera and LiDAR features into a bird's-eye-view (BEV) space and fuses them using a learned fusion with attention techniques, leveraging dense depth from stereo cameras to improve alignment and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR sensor data is used for object detection, then depth information is provided, but the depth data is sparse and alignment precision deteriorates
Solution Approach 1:
The patent combines LiDAR point cloud data with camera image data to create a fused representation. The LiDAR provides depth information while the camera provides rich visual features. By merging these two data sources through feature extraction and fusion modules, the system achieves both dense depth estimation and high measurement precision, resolving the contradiction between data density and precision.
Solution Approach 2:
The patent introduces an intermediary feature fusion module that mediates between LiDAR and camera data. This module extracts features from both modalities, aligns them through transformation, and fuses them to produce enhanced depth estimation. The intermediary fusion mechanism enables precise depth measurement while maintaining data density through the combined information.
2Measurement precision
If camera and LiDAR data are fused, then object detection precision improves, but computational complexity increases
Solution Approach 1:
The patent segments the fusion process into distinct modules: feature extraction module for LiDAR, feature extraction module for camera, transformation module for alignment, and fusion module for combination. This segmentation allows each module to process specific aspects of the data independently, improving computational efficiency while maintaining high detection precision through structured processing.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant features from each sensor modality rather than processing all raw data. The feature extraction modules identify and process only the essential characteristics needed for object detection, reducing computational complexity while maintaining the precision benefits of multi-sensor fusion.
3Measurement precision
If dense depth data is used, then object detection recall improves, but data processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and preprocessing actions on both LiDAR and camera data before the fusion step. By pre-processing the data to extract only essential features and prepare them for fusion, the system reduces the computational burden during the actual fusion process, thereby improving recall through dense depth data while minimizing processing time.
Data Source
AI summary
A perception system is disclosed. The perception system includes at least one memory configured to store machine executable instructions, and at least one processor configured to execute the stored executable instructions to: (i) extract camera features from stereo images; (ii) extract LiDAR features from a LiDAR point cloud; (iii) transform the camera features in a bird's-eye-view (BEV) space; (iv) transform the LiDAR features in the BEV space; and (v) fuse the transformed camera features and LiDAR features in the BEV space using a learned fusion with attention technique to generate the fused camera features and LiDAR features in the BEV space.