Piece-Wise LiDAR Network Structure for Sparse Long-Range Perception
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Solution Overview
Problem
Existing environment perception technologies face challenges in processing sparse point cloud data from LiDAR sensors, particularly at longer distance ranges, due to varying data density, which complicates long-range object detection and classification tasks.
Innovation Solution
A piece-wise network structure is employed, where each section of the network processes a specific distance range of point cloud data with varying density, producing finer details for closer ranges and coarser details for farther ranges, using a convolutional deep neural network to enhance environment perception accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single uniform network structure is used to process point cloud data across all distance ranges, then the device complexity is reduced, but the measurement precision and environment perception accuracy deteriorate due to varying data density
Solution Approach 1:
The point cloud processing network is segmented into multiple pieces, where each piece is responsible for processing a specific distance range (e.g., near, mid, far ranges). This segmentation allows each network piece to be optimized for its specific range's data density characteristics, improving overall perception accuracy while managing complexity through modular design
Solution Approach 2:
Different network configurations or processing strategies are applied to different spatial regions (distance ranges) of the point cloud data. Near-range data with high density receives different processing treatment compared to far-range data with low density, optimizing measurement precision for each local region
2Reliability
If point cloud data is processed in its original form without division, then the processing speed is maintained, but the reliability of long-range object detection deteriorates due to sparse data density
Solution Approach 1:
The point cloud data is divided into multiple distance ranges before processing, with each range handled by a dedicated network piece. This segmentation improves reliability for long-range detection by applying appropriate processing strategies for sparse data, while maintaining overall processing efficiency through parallel processing of multiple segments
Solution Approach 2:
Processing parameters such as network architecture, feature extraction depth, or aggregation strategies are changed based on the distance range and corresponding point density. For far-range sparse data, parameters are adjusted to enhance detection reliability without significantly increasing processing time
3Loss of information
If the network processes all distance ranges with the same detail level, then the device complexity is reduced, but the loss of information increases for both near and far ranges
Solution Approach 1:
The network outputs different levels of detail information tailored to each distance range: fine-grained detail for near-range objects where high density allows it, and coarse-grained detail for far-range objects where sparsity makes fine detail unavailable. This reduces information loss by matching output detail to input data quality
Solution Approach 2:
The network is divided into multiple pieces that independently process different distance ranges and produce range-specific output details. This segmentation preserves important information for each range without requiring a complex unified structure that would need to optimize for all ranges simultaneously
Data Source
AI summary
An apparatus and method for performing environment perception is described. An example technique may include receiving a point cloud from a sensor, such as a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor. The example techniques may further include dividing the point cloud into a plurality of distances ranges, processing the points in each distance range of the point cloud with a different section of a plurality of sections of a piece-wise network structure, and outputting environment perception data from the piece-wise network structure.