Low-Resolution 2D LIDAR Human Detection via Characteristic Functions

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Solution Overview

Problem

Existing human detection technologies face challenges with high-resolution LIDAR sensors being expensive and unsuitable for indoor use due to their size, while low-resolution sensors struggle to accurately sense human shapes due to insufficient output data.

Innovation Solution

A method and apparatus using a low-resolution 2D LIDAR sensor to derive a higher-order human characteristic function from LIDAR data, filtering and clustering points to identify human shapes, and determining objects as human based on extracted feature data compared to prestored data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a high-resolution LIDAR sensor is used, then measurement precision is improved, but device complexity and cost increase

Engineering Contradiction:

Improvehuman shape sensing accuracyVSAvoidequipment complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent changes the parameter of LIDAR resolution from high to low, and compensates by changing the data processing parameters (using higher-order polynomial curves and characteristic functions) to achieve accurate human shape identification despite the lower sensor resolution

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical/sensor-based solution (high-resolution LIDAR hardware) with a computational/mathematical solution (higher-order polynomial curve fitting and characteristic function derivation) to achieve the same measurement precision goal

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If a high-resolution LIDAR sensor is used, then measurement precision is improved, but cost increases

Engineering Contradiction:

Improvehuman shape sensing accuracyVSAvoidequipment cost

Core Design Contradiction:

Measurement precisionVSEase of manufacture

Solution Approach 1:

The patent changes the sensor resolution parameter from high to low, and compensates by using higher-order mathematical models (polynomial curves and characteristic functions) to extract accurate human shape information, thereby reducing equipment cost while maintaining measurement precision

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent uses a low-resolution, cheaper LIDAR sensor instead of an expensive high-resolution one, accepting that the raw data is less precise but compensating through computational methods to achieve the desired measurement accuracy

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Device complexity

If a low-resolution LIDAR sensor is used, then device complexity is reduced, but measurement precision deteriorates

Engineering Contradiction:

Improveequipment simplicityVSAvoidhuman shape sensing accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent changes the data processing parameters by using higher-order polynomial curves and characteristic functions to compensate for the low sensor resolution, thereby maintaining measurement precision despite using a simple low-resolution LIDAR device

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the need for complex high-resolution sensor hardware with a computational approach using higher-order mathematical models to achieve accurate human shape sensing from low-resolution data

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Measurement precision

If a high-resolution LIDAR sensor is used, then measurement precision is improved, but processing time increases

Engineering Contradiction:

Improvehuman shape sensing accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses higher-order polynomial curves (excessive mathematical complexity) to compensate for low sensor resolution, which actually reduces processing time compared to handling large volumes of high-resolution data, as less raw data needs to be processed

Inventive Principle:

Principle #16Partial or excessive action

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

Enables accurate human detection with reduced costs and complexity, suitable for indoor use, by deriving a higher-order human characteristic function from low-resolution LIDAR data, distinguishing humans from obstacles and minimizing processing time.

Implementation Method 1

receiving LIDAR data generated by reflecting a laser signal that continues to be transmitted to a search region from a plurality of objects in the search region

Methodology Applied

Scientific EffectLight reflection: Reflection

Data Source

PatentUS10366282B2Human detection apparatus and method using low-resolution two-dimensional (2D) light detection and ranging (LIDAR) sensor

Publication Date: 2019.07.30 DAEGU GYEONGBUK INSTITUTE OF SCIENCE AND TECHNOLOGY

AI summary

A human detection apparatus and method using low-resolution two-dimensional (2D) light detection and ranging (LIDAR) sensor are provided. The human detection method may include receiving LIDAR data generated by reflecting a laser signal that continues to be transmitted to a search region from a plurality of objects in the search region, clustering a plurality of points included in the received LIDAR data by the same objects based on a correlation between the plurality of points, deriving a characteristic function used to identify a shape of a human, based on the clustered points, and determining whether each of the objects is a human based on the derived characteristic function.