Point Cloud Segmentation for Adaptive Outlier Filtering
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Solution Overview
Problem
Existing methods for filtering data from sensors moving relative to objects, using fixed threshold values, often remove necessary data when the sensor is not parallel to the target, leading to inefficient data processing.
Innovation Solution
An information processing apparatus and method that acquires point group data from multiple sensor positions, divides it into segments, calculates the distribution of distances and angles, and removes outlier values based on these distributions to filter unnecessary data without removing essential information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fixed threshold filtering is applied to remove measurement data of static target objects, then communication traffic is reduced, but originally necessary data are removed when the sensor is not moving parallel to the target object
Solution Approach 1:
The patent divides point group data into multiple segments based on spatial distribution and movement characteristics. By segmenting the data, the system can apply different filtering strategies to different segments, preserving necessary data while removing redundant static object data. This segmentation allows the system to adapt to varying sensor movement patterns without using a single fixed threshold.
Solution Approach 2:
The patent transitions from fixed threshold filtering to dynamic threshold adjustment based on sensor movement characteristics. The filtering thresholds are adjusted according to the actual movement pattern of the sensor relative to target objects, enabling the system to maintain data quality while reducing communication traffic. This dynamic approach prevents the loss of necessary data that occurs with static threshold methods.
2Measurement precision
If measurement data of all measurement points are transmitted to the server, then data accuracy is maintained, but communication traffic increases
Solution Approach 1:
The patent extracts and removes measurement data corresponding to static target objects before transmission to the server. By identifying and extracting redundant data based on spatial distribution patterns and movement characteristics, the system maintains data accuracy for dynamic objects while significantly reducing communication traffic. This selective extraction preserves necessary information while eliminating unnecessary data transmission.
Solution Approach 2:
The patent changes filtering parameters dynamically based on sensor movement characteristics and target object properties. Instead of using fixed thresholds, the system adjusts filtering parameters according to actual measurement conditions, enabling optimal balance between data accuracy and communication efficiency. This parameter adaptation ensures that necessary data is preserved while redundant data is filtered out.
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 effectively filters out unnecessary data while preserving necessary data, improving data accuracy and reducing communication traffic in sensor systems, especially when the sensor is moving relative to the object.
Implementation Method 1
A sensor such as LiDAR can irradiate each measurement point of an object to be measured with a laser and calculate a distance to each measurement point, based on a time from the irradiation of the laser until light reception
Data Source
AI summary
An information processing apparatus according to the present disclosure includes: a first acquisition unit that acquires first point group data indicating a distance between a sensor and an object to be measured at a first measurement spot along a path; a second acquisition unit that acquires second point group data indicating a distance between a sensor and the object to be measured at a second measurement spot different from the first measurement spot along the path; a division unit that divides point group data including the first point group data and the second point group data into one or a plurality of segments; a distribution calculation unit that calculates a distribution of the point group data in the segment; and a removal unit that removes an outlier value from the point group data in the segment, based on the distribution.