FMCW-LiDAR Velocity Filtering for Stationary Point Cloud Generation
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Solution Overview
Problem
Existing methods for localization and mapping in autonomous moving bodies, such as self-driving cars and robots, face challenges with accurately distinguishing between stationary and moving objects, particularly due to the complexity and time-consuming nature of feature point extraction from camera images, and the uncertainty of information obtained from cameras in varying environmental conditions.
Innovation Solution
A data processing device equipped with a storage unit and processing circuit that acquires measurement data including position and velocity information from sensors like FMCW-LiDAR, allowing for the efficient recognition of stationary objects and generation of point cloud data that excludes moving objects, thereby facilitating accurate localization and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point extraction from camera images is used for localization and mapping, then object recognition can be performed, but processing time increases and accuracy decreases in varying environmental conditions
Solution Approach 1:
The patent replaces camera-based optical processing with FMCW-LiDAR-based electromagnetic wave processing. The FMCW-LiDAR measures distance and velocity directly through electromagnetic wave transmission and reception, eliminating the need for complex image feature extraction and processing, thus reducing processing time while improving accuracy
Solution Approach 2:
The patent changes the measurement parameters from optical intensity and color information (camera) to distance and velocity information (FMCW-LiDAR). By using frequency-modulated continuous wave technology, the system directly obtains precise distance and velocity parameters, which are more reliable for distinguishing stationary and moving objects regardless of environmental lighting conditions
2Reliability
If camera-based feature point matching is used for localization, then mapping can be generated, but reliability decreases when moving objects are present in the environment
Solution Approach 1:
The patent replaces camera-based optical recognition with FMCW-LiDAR-based electromagnetic wave measurement. The velocity information obtained through Doppler effect measurement allows direct identification of moving objects, preventing them from being incorrectly recognized as landmarks and thus improving localization reliability
Solution Approach 2:
The patent introduces velocity information as an intermediary parameter to distinguish between stationary landmarks and moving objects. By using velocity as a filtering criterion, the system can reliably identify which measurement points correspond to stationary objects suitable for localization and mapping
3Loss of information
If FMCW-LiDAR is used for measurement, then velocity information can be obtained, but device complexity increases
Solution Approach 1:
The patent employs FMCW-LiDAR that simultaneously provides both distance measurement and velocity measurement capabilities through a single device. The frequency-modulated continuous wave technology inherently provides both range and velocity information, eliminating the need for separate sensors and reducing overall system complexity despite the advanced technology used
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
This approach enables faster and more accurate generation of point cloud data, reducing processing time and improving the reliability of localization and mapping by using velocity information from sensors like FMCW-LiDAR to differentiate between stationary and moving objects, thus enhancing the accuracy of map generation and object recognition.
Implementation Method 1
FMCW-LiDAR combines a wide dynamic range and high resolution with respect to distance, is highly vibration resistant, and can measure the relative velocity between a sensor and a moving object
Data Source
AI summary
A data processing device includes: a storage device storing measurement data, including position information and velocity information about multiple measurement points in a space; and a processing circuit. The processing circuit acquires the measurement data from the storage device, recognizes measurement points on a stationary object from among the multiple measurement points on the basis of the velocity information, and generates point cloud data including the position information about the measurement points on a stationary object on the basis of a result of the recognition.