LiDAR BEV Multi-Object Detection for Sparse Overlap Scenes
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately detecting and tracking multiple objects in complex environments using LiDAR sensors, particularly due to sparse panoramic information and object overlapping issues, which affect the robustness and accuracy of 3D object detection.
Innovation Solution
A single-stage 3D multi-object detection apparatus utilizing a LiDAR sensor, which includes a data input module, a BEV image generation module, a learning module, and a localization module to generate bird's eye view images, perform deep learning, and regress 3D candidate boxes for accurate object detection, employing a compact CNN architecture to handle dense 2D representations of LiDAR point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LiDAR sensor is used to provide panoramic sparse measurements, then detection range and field of view are improved, but measurement precision and object detection accuracy deteriorate due to sparse point density distribution
Solution Approach 1:
The patent transforms sparse 3D point cloud data from LiDAR into dense 2D Bird's Eye View images through projection and interpolation. This dimensionality change from 3D sparse to 2D dense representation allows standard 2D CNN architectures to process the data effectively, resolving the contradiction between wide coverage and detection accuracy.
Solution Approach 2:
The patent introduces BEV images as an intermediary representation between raw LiDAR point clouds and object detection results. This intermediary dense 2D representation bridges the gap between sparse panoramic measurements and accurate object detection, enabling precise localization while maintaining wide field of view.
2Measurement precision
If complex 3D detection frameworks are implemented to improve object detection accuracy, then measurement precision is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex 3D detection mechanisms with a simpler 2D CNN-based approach. By projecting 3D point clouds into 2D BEV images, the system can use efficient 2D convolutional networks instead of computationally intensive 3D detection frameworks, reducing device complexity while maintaining accuracy.
Solution Approach 2:
The patent segments the detection process into distinct modules: BEV image generation from point clouds, deep learning-based feature extraction from BEV images, and regression/localization to obtain 3D bounding boxes. This segmentation allows each module to be optimized independently, simplifying the overall system while achieving high accuracy.
3Speed
If real-time processing is implemented to improve response speed, then processing speed is improved, but measurement precision may deteriorate due to reduced processing time
Solution Approach 1:
The patent performs preliminary processing by generating BEV images from raw point clouds before object detection. This preprocessing step organizes sparse 3D data into a structured dense 2D format that CNNs can process efficiently, enabling real-time processing without sacrificing localization precision in the subsequent detection stages.
Data Source
AI summary
According to at least one embodiment, the present disclosure provides an apparatus for single-stage three-dimensional (3D) multi-object detection by using a LiDAR sensor to detect 3D multiple objects, comprising: a data input module configured to receive raw point cloud data from the LiDAR sensor; a BEV image generating module configured to generate bird's eye view (BEV) images from the raw point cloud data; a learning module configured to perform a deep learning algorithm-based learning task to extract a fine-grained feature image from the BEV images; and a localization module configured to perform a regression operation and a localization operation to find 3D candidate boxes and classes corresponding to the 3D candidate boxes for detecting 3D objects from the fine-grained feature image.