Rectilinear LIDAR Image Processing for Accurate Object Classification

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Solution Overview

Problem

Existing LIDAR systems face challenges in providing robust distance accuracy down to a few cm at an economical cost, especially in varying conditions, and struggle to provide comprehensive environmental data, including distant objects, which is crucial for vehicle navigation and object identification.

Innovation Solution

The system employs kernel-based image processing techniques on LIDAR data, utilizing dedicated circuitry and AI coprocessors to correlate LIDAR and color pixels, enabling robust distance measurement and object identification, particularly through rectilinear image reconstruction and classification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional LIDAR processing methods are used, then system cost is reduced, but distance measurement accuracy and robustness deteriorate

Engineering Contradiction:

Improvedistance measurement accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary processing layer that converts LIDAR point cloud data into rectilinear images, which then can be processed using standard image processing techniques. This intermediary representation enables the use of well-established, computationally efficient algorithms while maintaining measurement accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces complex 3D point cloud processing mechanisms with 2D image processing mechanisms. By transforming the data representation from three-dimensional point clouds to two-dimensional rectilinear images, the system可以利用 simpler, more efficient image processing algorithms while achieving comparable or superior measurement accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of information

If comprehensive environmental data is collected, then object identification capability is improved, but computational resource requirements increase

Engineering Contradiction:

Improveenvironmental information completenessVSAvoidcomputational energy consumption

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent extracts and separates different types of information from the LIDAR data stream, processing only the necessary components for specific tasks. By extracting relevant environmental features and organizing them into structured image formats, the system reduces computational overhead while maintaining information completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent performs preliminary organization and structuring of LIDAR data into rectilinear image format before main processing operations. This preliminary action prepares the data in an optimal format for subsequent analysis, reducing the computational burden during real-time object identification and environmental mapping.

Inventive Principle:

Principle #10Preliminary action

3Speed

If real-time LIDAR analysis is performed, then navigation response time is improved, but processing accuracy may deteriorate due to computational constraints

Engineering Contradiction:

Improveprocessing speedVSAvoidanalysis accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The patent implements periodic processing cycles where LIDAR data is continuously captured and processed in structured intervals. This periodic approach allows the system to maintain real-time responsiveness while allocating sufficient computational resources for accurate analysis within each processing cycle, balancing speed and precision requirements.

Inventive Principle:

Principle #19Periodic 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

This approach enhances LIDAR systems' accuracy and computational efficiency, allowing for real-time analysis of 3D environments, including object classification and navigation aids, reducing computational resources needed.

Implementation Method 1

A LIDAR system measures the distance to an object by irradiating a landscape with pulses from a laser, and then measuring the time for photons to travel to an object and return after reflection

Methodology Applied

Scientific EffectTime of flight: Time of Flight

Implementation Method 2

measuring the time for photons to travel to an object and return after reflection

Methodology Applied

Scientific EffectLight reflection: Reflection

Data Source

PatentUS12578475B2Processing of LIDAR images

Publication Date: 2026.03.17 OUSTER INC

AI summary

Systems and methods are provided for processing lidar data. The lidar data can be obtained in a particular manner that allows reconstruction of rectilinear images for which image processing can be applied from image to image. For instance, kernel-based image processing techniques can be used. Such processing techniques can use neighboring lidar and/or associated color pixels to adjust various values associated with the lidar signals. Such image processing of lidar and color pixels can be performed by dedicated circuitry, which may be on a same integrated circuit. Further, lidar pixels can be correlated to each other. For instance, classification techniques can identify lidar and/or associated color pixels as corresponding to the same object. The classification can be performed by an artificial intelligence (AI) coprocessor. Image processing techniques and classification techniques can be combined into a single system.