Moving Object Detection from Speed-Added Point Cloud Data
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Solution Overview
Problem
Existing object detection systems using three-dimensional point cloud data from LiDAR face increased calculation loads, which can hinder efficient detection processing.
Innovation Solution
An object detection apparatus that includes a detector to irradiate a three-dimensional space with electromagnetic waves, a microprocessor to process point cloud data by calculating absolute moving speeds, classify data into moving and stationary points, convert three-dimensional positions to two-dimensional, and generate speed-added data for accurate object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is used without change for detection processing, then detection accuracy is maintained, but calculation load increases
Solution Approach 1:
The patent segments point cloud data into stationary and moving objects based on speed information. By dividing the data processing into separate pathways - where stationary objects are filtered out and only moving objects undergo full detection processing - the system maintains detection accuracy for moving objects while significantly reducing the overall calculation load.
Solution Approach 2:
The patent extracts speed information from point cloud data and uses it to identify and separate moving objects from stationary background. By extracting only the relevant moving objects for detailed detection processing while excluding stationary objects, the system reduces calculation load without compromising detection accuracy for moving targets.
2Measurement precision
If three-dimensional position information is processed, then detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent adds a speed dimension to the traditional three-dimensional point cloud data, creating four-dimensional data that includes velocity information. This additional dimension enables automatic separation of moving and stationary objects, simplifying the detection process by allowing the system to focus computational resources on moving objects only, thereby reducing processing complexity while maintaining or improving detection accuracy.
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
Reduces calculation load while improving detection accuracy of moving objects by classifying and clustering based on speed information, allowing for precise identification of individual objects even when closely proximate.
Implementation Method 1
a detector mounted on a mobile body, and configured to irradiate a three-dimensional space in a surrounding of a mobile body with an electromagnetic wave to detect an exterior environment situation in the surrounding of the mobile body based on a reflected wave
Data Source
AI summary
An object detection apparatus includes: a detector configured to irradiate with an electromagnetic wave to detect an exterior environment situation in the surrounding of a mobile body based on a reflected wave; and a microprocessor configured to perform: acquiring point cloud data from the detector; classifying the point cloud data into moving point cloud data and stationary point cloud data, the moving point cloud data corresponding to measurement points where absolute values of an absolute moving speeds are equal to or higher than a predetermined speed; converting the three-dimensional position information of measurement points corresponding to the moving point cloud data into two-dimensional position information; generating speed added data by adding the absolute moving speed corresponding to the measurement points to the two-dimensional position information; and detecting an object included in the surroundings of the mobile body, based on the speed added data.