Lidar Superpixel Processing for Range-Accurate Object Detection
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Solution Overview
Problem
Conventional lidar systems face challenges in data quality and detection accuracy due to fixed pixel sizes, leading to blurring and distortions when dealing with objects of varying range and intensity, which affects autonomous vehicle operations.
Innovation Solution
The implementation of a novel method that arranges pixels in a grid based on correlations between range and intensity values, identifies regions of interest, and generates superpixels with variable sizes and positions to improve data integration and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed pixel sizes are used in conventional lidar systems, then device complexity is reduced, but measurement precision deteriorates due to blurring and distortions when dealing with objects of varying range and intensity
Solution Approach 1:
The patent segments the lidar data processing into distinct stages: initial pixel grid creation, region of interest identification through correlation analysis, and superpixel generation. This segmentation allows fixed-size initial processing followed by adaptive refinement only where needed, improving precision without proportionally increasing overall complexity.
Solution Approach 2:
The patent applies local quality by using variable-sized superpixels that adapt to local characteristics of the scene. Regions with high correlation and varying intensity receive larger superpixels for better integration, while uniform regions use smaller superpixels, optimizing measurement precision locally without uniformly increasing complexity everywhere.
2Measurement precision
If variable sized superpixels are generated based on correlations between range and intensity values, then measurement precision is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent performs preliminary action by first creating a standard pixel grid and identifying regions of interest using correlation analysis before generating variable-sized superpixels. This preliminary structuring simplifies the subsequent superpixel generation process and makes the complexity manageable through staged processing.
Solution Approach 2:
The patent changes parameters dynamically by adjusting superpixel size based on correlation coefficients and intensity variations. This parameter adaptation is performed systematically through defined algorithms that evaluate local data characteristics and adjust superpixel boundaries accordingly, making the complexity systematic rather than arbitrary.
3Measurement precision
If pixels are arranged in a grid and regions of interest are identified through correlation analysis, then detection quality is improved, but loss of time increases due to additional processing operations
Solution Approach 1:
The patent extracts only the essential processing steps: grid arrangement, correlation-based region identification, and superpixel generation. By taking out and focusing on these critical operations while eliminating unnecessary intermediate processing steps, the patent achieves improved detection quality with minimized time loss.
Solution Approach 2:
The patent merges multiple processing objectives into the superpixel generation step, which simultaneously performs data integration, correlation analysis results application, and feature extraction. This merging reduces the number of separate processing operations and minimizes total processing time while maintaining detection quality.
Data Source
AI summary
Disclosed herein are systems, methods, and computer program products for operating a lidar system. The methods comprise: arranging, by the processor, a plurality of pixels in a grid (the pixels comprising result values generated from processing waveforms produced by photodetectors of the lidar system); identifying, by the processor, a first region of interest in the grid based on correlations between range values associated with the plurality of pixels and/or correlations between intensity values associated with the plurality of pixels; combining, by the processor, result values associated with pixels located within the first region of interest to produce first feature value(s); and generating, by the processor, a first superpixel having value(s) set to the first feature value(s).