FMCW LiDAR Point Cloud Correction for VR Interaction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for input operations based on user gestures or object movements face issues with output errors and processing time due to large errors in three-dimensional position sensing, leading to discomfort and reduced motion and position resolution, particularly when using low-pass filters or downsampling to reduce data processing.
Innovation Solution
An information processing apparatus and method utilizing a photodetection ranging unit with frequency modulated continuous wave (FMCW) LiDAR to output point clouds with velocity information and three-dimensional coordinates, coupled with a recognition unit for determining designated areas and a correction unit to refine these coordinates, improving stability and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If low-pass filter or downsampling is used to reduce processed data, then processing time is reduced, but measurement precision and resolution of motion and position information decrease
Solution Approach 1:
The system performs preliminary classification of point clouds into moving objects and background before processing. By identifying and separating moving objects in advance, the system can focus computational resources on relevant data, reducing the need for aggressive downsampling while maintaining processing efficiency.
Solution Approach 2:
The point cloud data is segmented into different categories (moving objects, background, static elements). This segmentation allows the system to process only the necessary portions of data with high precision while using simplified processing for background elements, thereby maintaining measurement precision for critical motion data without excessive processing time.
2Measurement precision
If three-dimensional position sensor data is processed with high precision, then measurement precision is improved, but processing time increases and responsiveness deteriorates
Solution Approach 1:
The system performs preliminary classification of point clouds into moving objects and background before detailed processing. By identifying and separating moving objects in advance, the system can focus computational resources on relevant data, reducing the need for aggressive downsampling while maintaining processing efficiency.
Solution Approach 2:
The system dynamically adjusts processing intensity based on the characteristics of detected objects. For moving objects that require high precision tracking, the system applies more sophisticated processing algorithms, while for static background elements, simpler processing is used, thereby optimizing the balance between precision and responsiveness.
3Productivity
If the number of processed data points is reduced, then processing speed is improved, but reliability of position and motion information decreases
Solution Approach 1:
The point cloud data is segmented into different categories (moving objects, background, static elements). This segmentation allows the system to process only the necessary portions of data with high precision while using simplified processing for background elements, thereby maintaining measurement precision for critical motion data without excessive processing time.
Solution Approach 2:
The system performs preliminary classification of point clouds into moving objects and background before detailed processing. By identifying and separating moving objects in advance, the system can focus computational resources on relevant data, reducing the need for aggressive downsampling while maintaining processing efficiency.
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 display stability and responsiveness by reducing the number of processed data points and improving position and posture estimation, allowing for more precise and comfortable interaction with virtual objects in virtual reality and augmented reality applications.
Implementation Method 1
a photodetection ranging unit using a frequency modulated continuous wave (FMCW) that outputs a point cloud including velocity information and three-dimensional coordinates based on a reflected light signal
Implementation Method 2
a reception signal reflected by an object and received
Data Source
AI summary
An information processing apparatus according to an embodiment includes: a recognition unit (122) configured to perform recognition processing on the basis of a point cloud output from a photodetection ranging unit (11) using a frequency modulated continuous wave to determine a designated area in a real object, the photodetection ranging unit being configured to output the point cloud including velocity information and three-dimensional coordinates of the point cloud on the basis of a reception signal reflected by an object and received, and configured to output three-dimensional recognition information including information indicating the determined designated area, and a correction unit (125) configured to correct three-dimensional coordinates of the designated area in the point cloud on the basis of the three-dimensional recognition information output by the recognition unit.