Point Cloud Velocity Analysis for Predicted Object Behavior Detection
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Solution Overview
Problem
Existing autonomous and robotic devices face challenges in accurately perceiving and navigating their environment using light-based sensors, particularly in determining the range, velocity, and behavior of objects, which can impact their operational efficiency and safety.
Innovation Solution
Implementing a light-based sensor system that utilizes Frequency Modulated Continuous Wave (FMCW) LiDAR technology to detect and process point cloud information, enabling precise determination of object parameters such as range, velocity, and orientation, and integrating this information into navigation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If light-based sensors (cameras, LiDAR) are used to determine range and velocity of objects, then environmental perception capability is improved, but measurement precision and reliability of behavior detection deteriorate
Solution Approach 1:
The system segments point cloud data into multiple clusters representing different objects, then analyzes velocity information within each cluster separately. This segmentation allows precise behavior detection for each object while maintaining comprehensive environmental perception across the entire scene.
Solution Approach 2:
The patent introduces point cloud velocity information as an intermediary parameter between raw sensor data and behavior detection. By calculating velocity from point cloud data and using it to characterize object behavior, the system bridges the gap between environmental perception and precise behavior analysis.
2Measurement precision
If FMCW LiDAR technology is implemented to detect point cloud information, then measurement precision of object parameters is improved, but device complexity increases
Solution Approach 1:
The FMCW LiDAR system performs multiple functions simultaneously: it captures spatial point cloud information for environmental mapping and extracts velocity information for behavior detection. This multi-functionality reduces the need for separate sensors, thereby managing complexity while improving measurement precision.
Solution Approach 2:
The system changes the operational parameters of the FMCW LiDAR to optimize for both spatial and velocity measurements. By adjusting frequency modulation characteristics and processing parameters, the system achieves high precision in object parameter detection while maintaining manageable device complexity through parameter optimization rather than hardware multiplication.
3Measurement precision
If point cloud information is processed to determine velocity and behavior, then behavior detection accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary clustering of point cloud data into object-specific groups before velocity analysis. By pre-organizing the data structure and identifying object boundaries in advance, the system reduces the computational complexity of subsequent velocity calculations and behavior detection, thereby decreasing processing time while maintaining accuracy.
Solution Approach 2:
The patent applies velocity analysis selectively to specific object clusters rather than processing all point cloud data uniformly. By focusing computational resources on relevant objects and their critical motion characteristics, the system achieves high behavior detection accuracy for important targets while reducing overall processing time through selective rather than exhaustive analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and reliability of autonomous navigation by providing detailed environmental perception, allowing for safer and more efficient operation of vehicles and robots.
Implementation Method 1
Implementing a light-based sensor system that utilizes Frequency Modulated Continuous Wave (FMCW) LiDAR technology to detect and process point cloud information
Implementation Method 2
utilizes light-based sensors, such as image sensors, e.g., cameras, and/or Light Detection and Ranging (LiDAR) sensors
Data Source
AI summary
For example, a processor may be configured to process Point Cloud (PC) information including velocity information corresponding to a plurality of points. For example, velocity information corresponding to a point of the plurality of points may include a velocity value corresponding to the point. For example, the processor may be configured to identify a relative movement between a first element of a detected target and a second element of the detected target based on a first plurality of velocity values and a second plurality of velocity values. For example, the first plurality of velocity values may correspond to a plurality of first points corresponding to the first element, and the second plurality of velocity values may correspond to a plurality of second points corresponding to the second element. For example, the processor may determine a predicted behavior detection corresponding to the detected target, for example, based on the relative movement.