Adaptive Camera View Selection for Accurate Object Recognition

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Solution Overview

Problem

Existing robotic systems face challenges in accurately recognizing the pose and surface features of objects, leading to imprecise control and the need for a large number of images to achieve reliable object manipulation.

Innovation Solution

A method utilizing reinforcement learning to train an agent that selects optimal camera perspectives for image recording, based on the change in confidence in object information output by a machine learning model, thereby improving data efficiency and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple images are recorded to improve object recognition accuracy, then measurement precision is improved, but the quantity of data and time required increase

Engineering Contradiction:

Improveobject recognition accuracyVSAvoidtime to record sufficient images

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system uses confidence values from the machine learning model as feedback to guide image acquisition. After each image is recorded, the confidence in object information is evaluated, and this feedback determines whether additional images are needed and from which perspectives, thereby optimizing the number of images required for accurate recognition

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The image recording process is made dynamic and adaptive rather than static and predetermined. The system adjusts the number and perspectives of images to be recorded based on real-time confidence assessments, allowing the process to adapt to the specific characteristics of each object and recognition task

Inventive Principle:

Principle #15Dynamics

2Device complexity

If images are recorded from predefined perspectives, then device complexity is reduced, but measurement precision deteriorates

Engineering Contradiction:

Improvecamera positioning complexityVSAvoidobject information accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system performs preliminary confidence assessment using initial images to determine what additional information is needed before finalizing the recognition. This preliminary evaluation guides the selection of subsequent image perspectives, ensuring that images are captured from the most informative angles rather than predetermined positions

Inventive Principle:

Principle #10Preliminary action

3Productivity

If heuristic methods are used to select perspectives, then productivity is improved, but measurement precision deteriorates

Engineering Contradiction:

Improveimage selection efficiencyVSAvoidinformation gain accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

Instead of using heuristic rules for perspective selection, the system employs feedback from the machine learning model's confidence assessment. The confidence values provide objective feedback on what information is missing or uncertain, guiding the selection of subsequent image perspectives to maximize actual information gain rather than following predetermined heuristics

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12269181B2Method for ascertaining object information with the aid of image data

Publication Date: 2025.04.08 ROBERT BOSCH GMBH

AI summary

A method for ascertaining object information from image data. The method includes training an agent with the aid of reinforcement learning, successively recording images according to actions that are output by the agent, after each recording, the agent obtaining information, generated from the previously recorded images, concerning the location of surface points of an object as state information, and ascertaining the object information from the recorded images with the aid of the machine learning model.