Point Cloud Classification via Gravity Vector Angles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for classifying measuring points in a point cloud from LIDAR, radar, or camera sensors require high computing power and are not real-time capable, leading to inefficiencies in object detection with high false-positive and false-negative rates.
Innovation Solution
A method utilizing a control unit, such as an FPGA or ASIC, to calculate local surface vectors and classify measuring points based on angles with respect to a gravity vector, employing maximal and standardized surface vectors to differentiate between ground and non-ground points, thereby reducing CPU load and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification methods are used, then classification accuracy is improved, but computing power requirements increase and real-time processing becomes impossible
Solution Approach 1:
The patent segments the classification process into distinct phases: first calculating local surface vectors for each measuring point, then computing angles with the gravity vector, and finally classifying based on threshold comparisons. This segmentation allows each step to be optimized independently, enabling real-time processing while maintaining accuracy.
Solution Approach 2:
The patent transforms the classification problem from direct geometric analysis to angular parameter analysis. By computing angles between local surface vectors and the gravity vector, and comparing these angles against threshold values, the method simplifies the computational complexity while preserving classification precision, thus enabling real-time processing.
2Reliability
If conventional classification methods are used, then comprehensive object detection is achieved, but false-positive and false-negative rates remain high
Solution Approach 1:
The patent incorporates a feedback mechanism where classification results are continuously refined. By systematically processing each measuring point through consistent angular comparison with the gravity vector, the method reduces classification errors and improves reliability without requiring excessive computational resources.
Solution Approach 2:
The patent changes the classification parameter from complex geometric relationships to simple angular comparisons. This parameter transformation reduces both false-positive and false-negative rates by providing a more robust and consistent classification criterion that is less sensitive to variations in point cloud data.
3Manufacturing precision
If complex classification algorithms are implemented, then processing thoroughness is improved, but device complexity and technical effort increase
Solution Approach 1:
The patent extracts the essential feature for classification—the angle between the local surface vector and the gravity vector—while discarding unnecessary computational complexity. This extraction approach maintains processing thoroughness by focusing on the most discriminative parameter, thereby reducing device complexity and technical implementation effort.
Solution Approach 2:
The patent simplifies the classification algorithm by changing from complex multi-parameter analysis to a single angular parameter comparison. This parameter change maintains processing thoroughness through systematic angular threshold evaluation while significantly reducing algorithmic complexity and implementation difficulty.
Data Source
AI summary
A method for classifying measuring points of a point cloud ascertained by at least one sensor, in particular, a point cloud ascertained from a LIDAR sensor, a radar sensor and/or a camera sensor, via a control unit. Local surface vectors to adjacent measuring points are ascertained for each measuring point of the point cloud. For each local surface vector, respectively one angle is calculated between the local surface vectors with respect to a gravity vector. A maximal surface vector having a maximal angle with respect to the gravity vector and a standardized surface vector are ascertained for each measuring point of the point cloud based on the calculated angles. Each measuring point of the point cloud includes a standardized surface vector and/or includes a maximal surface vector having an angle with respect to the gravity vector above a limiting value being classified as a non-ground point.