LiDAR Static Object Classification Using Convex Hull Stability
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Solution Overview
Problem
Existing techniques for classifying static objects using LiDAR data are computationally inefficient and prone to false positives, impacting the operation of machines such as autonomous vehicles.
Innovation Solution
An enhanced approach using LiDAR data for static object classification involves comparing stable features over time by maintaining a buffer with LiDAR object data and applying a SE transformation to determine the probability of an object being static, based on convex hull features and a feature weight matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static object classification techniques are used, then object detection can be performed, but computational efficiency is poor and false positives occur
Solution Approach 1:
The patent segments the object classification problem into distinct phases: initial detection phase using simplified criteria and confirmation phase using rigorous statistical testing. This segmentation allows computationally expensive methods to be applied only when necessary, improving overall efficiency while maintaining accuracy.
Solution Approach 2:
The patent applies preliminary filtering using convex hull features and basic motion criteria before conducting full statistical analysis. This preliminary action eliminates obvious false positives early in the process, reducing the computational burden of more intensive verification methods.
2Productivity
If traditional static object classification techniques are used, then object detection can be performed, but false positives increase classification errors
Solution Approach 1:
The patent implements feedback through iterative hypothesis testing where classification results are continuously refined. The system uses statistical feedback from multiple measurements to adjust confidence levels and re-evaluate borderline cases, reducing false positives while maintaining detection speed.
Solution Approach 2:
The patent replaces traditional mechanical motion-based classification with statistical field-based analysis using LiDAR point cloud data. This substitution enables more accurate detection of static objects by analyzing spatial distributions rather than relying solely on motion cues, thereby reducing false positives.
3Measurement precision
If computationally intensive methods are used to improve accuracy, then classification precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by using simplified classification criteria for the majority of objects and reserving computationally intensive methods for ambiguous cases. This approach achieves sufficient accuracy for most detections while minimizing processing time, applying excessive computational effort only where necessary.
Solution Approach 2:
The patent dynamically changes processing parameters based on object characteristics and confidence levels. For high-confidence detections, minimal processing is applied, while low-confidence cases trigger more intensive analysis. This parameter adaptation optimizes the balance between accuracy and processing time.
Data Source
AI summary
Devices, systems, and methods are provided for classifying detected objects as static or dynamic. A device may determine first light detection and ranging (LIDAR) data associated with a convex hull of an object at a first time, and determine second LIDAR data associated with the convex hull at a second time after the first time. The device may generate, based on the first LIDAR data and the second LIDAR data, a vector including values of features associated with the first convex hull and the second convex hull. The device may determine, based on the vector, a probability that the object is static. The device may operate a machine based on the probability that the object is static.