Lidar Calibration Standard Detection in Unstructured Point Clouds

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Solution Overview

Problem

Existing methods for extrinsic calibration of sensor pairs, such as lidar and camera systems, are limited by the need for controlled environments and are time-consuming, making them impractical for field applications and prone to misalignment over time.

Innovation Solution

A method for automatically detecting a calibration standard in unstructured lidar point clouds using contour and distribution metrics to identify and define the boundaries of a calibration standard, enabling efficient extrinsic calibration in real-world scenarios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional extrinsic calibration methods are used with controlled environments and metrological methods, then measurement precision is improved, but productivity deteriorates due to time-consuming procedures and inability to perform in field

Engineering Contradiction:

Improvesensor pose measurement precisionVSAvoidcalibration speed and field deployability

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-calibration by automatically detecting the calibration standard in the point cloud and computing sensor poses without requiring external survey equipment or controlled environment setup. The sensor platform itself captures the data and processes it to determine its own calibration parameters, eliminating the need for external metrological methods and enabling field deployment.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The calibration standard is pre-positioned in the scene before the sensor platform arrives. This preliminary placement of the calibration target allows the system to perform rapid automatic calibration upon arrival, eliminating the need for time-consuming setup procedures during the calibration process itself.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If meticulous staging of calibration targets and fiducials is used, then measurement precision is improved, but device complexity and time consumption increase

Engineering Contradiction:

Improvesensor pose measurement precisionVSAvoidcalibration setup complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system automatically detects and identifies the calibration standard in the point cloud data without requiring manual setup or complex staging procedures. The automatic detection algorithm processes the unstructured point cloud to locate the calibration target and extract its features, eliminating the need for meticulous manual arrangement of fiducials and reducing setup complexity to minimal target placement.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If sensor platform is disassembled to enable access for calibration, then measurement precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvesensor pose measurement precisionVSAvoidcalibration accessibility

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The calibration system operates autonomously on the sensor platform itself, using the platform's own sensors to capture point cloud data and compute calibration parameters. This self-contained approach eliminates the need to disassemble the sensor platform or gain physical access to sensor mounting locations, allowing calibration to be performed in-situ during normal operation.

Inventive Principle:

Principle #25Self-service

4Measurement precision

If external survey methods are used to locate sensors, then measurement precision is improved, but loss of time increases due to inability to perform in field

Engineering Contradiction:

Improvesensor position and orientation accuracyVSAvoidcalibration time and service interruption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The sensor platform performs its own calibration by capturing point cloud data of the calibration standard and automatically computing its pose relative to the standard. This eliminates the need for external survey teams, specialized equipment, and controlled environment setups, enabling calibration to be performed rapidly in the field without pulling the sensor platform from service.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The calibration standard is pre-positioned in the operational environment before calibration is needed. This preliminary setup allows the system to perform rapid automatic calibration at any time during field operation, eliminating time losses associated with transporting external survey equipment and setting up controlled calibration environments.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12504522B2Automatic detection of a calibration standard in unstructured lidar point clouds

Publication Date: 2025.12.23 THE BOEING CO

AI summary

Systems and methods include obtaining point cloud data representing a point cloud; selecting a subset of the point cloud data based at least in part on a contour metric; grouping sets of points of the subset of the point cloud into one or more clusters based at least in part on one or more distance metrics; for a cluster that satisfies one or more cluster size criteria based on dimensions of a calibration standard, determining whether a distribution of signal intensities of points of the cluster satisfies a distribution criterion; based on a determination that the distribution of signal intensities of points satisfies the distribution criterion, determining boundaries of a region that represents the calibration standard; and storing data identifying a set of points of the point cloud that correspond to the calibration standard.