Robot Range Sensor Calibration Using Point Cloud Alignment

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Solution Overview

Problem

Existing calibration methods for range sensors on robots require skilled technicians and specialized environments, making real-time calibration challenging outside manufacturing facilities.

Innovation Solution

A method for real-time calibration of multiple range sensors on a robot using self-calibration and cross-calibration techniques, aligning point clouds from multiple sensors to achieve accurate sensor alignment without external reference objects or specialized environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional calibration methods are used, then measurement precision is improved, but device complexity and ease of operation deteriorate due to requiring skilled technicians and specialized environments

Engineering Contradiction:

Improvesensor calibration accuracyVSAvoidcalibration operation simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-calibration by automatically aligning point clouds from multiple sensors using ICP algorithms and transform calculations, eliminating the need for skilled technicians to manually calibrate sensors in specialized environments

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts calibration parameters through real-time computation of rotational and translational transforms based on point cloud data, allowing adaptation to different sensor configurations and environmental conditions without manual intervention

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional calibration methods are used, then measurement precision is improved, but device complexity worsens due to requiring specialized equipment and environments

Engineering Contradiction:

Improvesensor calibration accuracyVSAvoidcalibration system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system uses the robot's own multiple sensors and environment data to perform calibration, eliminating the need for external calibration equipment and specialized facilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The calibration system works with multiple types of sensors (LiDAR, cameras, depth sensors) and can operate in various environments using natural features, making the system universally applicable without specialized equipment

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Adaptability or versatility

If real-time calibration is implemented, then adaptability is improved, but use of energy worsens due to continuous processing of point cloud data

Engineering Contradiction:

Improvereal-time calibration capabilityVSAvoidprocessing energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs calibration operations selectively based on detected environmental features and sensor data quality, rather than continuously processing all data, reducing energy consumption while maintaining real-time adaptability when needed

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12570003B2Systems, and methods for real time calibration of multiple range sensors on a robot

Publication Date: 2026.03.10 BRAIN CORP

AI summary

Systems and methods for systems and methods for real time calibration of multiple range sensors on a robot are disclosed herein. According to at least one non-limiting exemplary embodiment, methods for self-calibration are used to independently correct rotational errors in a pose of a sensor. In some instances, the self-calibration may further yield errors along at least one translational axis. According to at least one non-limiting exemplary embodiment, methods for cross-calibration are used to correct translational errors and to ensure all sensors on a robot agree on perceived locations of objects.