Rangefinding Sensor Data Extraction for Distance Measurement Accuracy
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Solution Overview
Problem
Existing distance measurement technologies face challenges in accurately determining the distance to an object, especially when the object's state in the image is complex, leading to inaccuracies in rangefinding point arrangements.
Innovation Solution
An information processing apparatus and method that extracts sensor data corresponding to an object region in an imaged image using a rangefinding sensor, allowing for precise distance calculation by setting extraction conditions based on the recognized object, such as clustering and threshold settings, to accurately match point cloud data with the object's image representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rangefinding points are set in the object region based on object recognition, then distance measurement can be performed, but measurement precision deteriorates when the object state is complex
Solution Approach 1:
The patent extracts only the necessary rangefinding points that correspond to the recognized object from the entire point cloud data, separating relevant measurement data from irrelevant background data. This extraction process improves measurement precision by focusing computational resources on object-related points while filtering out noise from other regions.
Solution Approach 2:
The patent segments the point cloud data into object-related regions and non-object regions based on object recognition results. By dividing the measurement space and applying different processing strategies to different segments, the system maintains high measurement precision for objects while handling complex states through region-specific analysis.
2Measurement precision
If sensor data extraction is performed without object recognition, then processing is simpler, but distance measurement accuracy deteriorates
Solution Approach 1:
The patent performs object recognition as a preliminary action before extracting rangefinding points and performing distance measurement. This preliminary classification of the scene into object and non-object regions enables subsequent precise extraction of measurement data, improving accuracy while organizing the processing workflow into manageable stages.
Solution Approach 2:
The patent introduces object recognition results as an intermediary that bridges the gap between raw sensor data and precise distance measurement. This intermediary layer provides semantic information about objects, enabling the system to selectively process relevant data points and achieve high measurement accuracy without overwhelming computational complexity.
3Productivity
If all sensor data is processed for distance measurement, then comprehensive coverage is achieved, but processing time increases
Solution Approach 1:
The patent extracts only the subset of sensor data that corresponds to recognized objects, removing unnecessary data processing. By extracting only object-related rangefinding points from the complete point cloud, the system significantly reduces processing time while maintaining comprehensive coverage of all objects in the scene.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of sensor data (object-related points) rather than the entire dataset. This selective processing approach achieves sufficient measurement coverage for all objects while avoiding the time cost of processing irrelevant background points.
Data Source
AI summary
The present technology relates to an information processing apparatus, an information processing method, and a program capable of obtaining a distance to an object more accurately.An extraction unit extracts, on the basis of an object recognised in an imaged image obtained by a camera, sensor data corresponding to an object region including an object in the imaged image among sensor data obtained by a rangefinding sensor. The present technology can be applied to an evaluation apparatus for distance information, for example.