Pixelwise Depth Map Filtering for Robot Sensor Confidence
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Solution Overview
Problem
Robotic systems face challenges in generating accurate and complete depth maps due to varying accuracy levels and resolutions from different sensors, leading to incomplete or inaccurate depth information, which affects navigation and interaction with the environment.
Innovation Solution
A pixelwise filterable depth map is generated by associating each pixel depth with a confidence level based on the sensor used, and corroborating depth information from multiple sensors, allowing for operation-specific depth maps by comparing pixelwise confidence levels with thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If depth information from multiple sensors with varying accuracy levels is combined, then the completeness of depth map coverage is improved, but the accuracy and reliability of depth information deteriorates due to inconsistent resolution and accuracy levels
Solution Approach 1:
The depth map is segmented into multiple regions, each corresponding to a specific sensor's field of view and accuracy characteristics. Each region is labeled with metadata indicating the sensor source and confidence level, allowing the system to selectively use depth information from different sensors based on spatial location and operational requirements.
Solution Approach 2:
Different regions of the depth map are assigned different quality attributes based on the sensor providing the data. High-accuracy sensors provide depth information for critical regions requiring precision, while lower-accuracy sensors fill in peripheral or less critical areas, optimizing the overall depth map quality for each specific region.
2Device complexity
If a single depth map is generated for all robot operations, then the system complexity is reduced, but the adaptability to different robot operations deteriorates as different operations require different confidence levels
Solution Approach 1:
The system generates multiple depth maps dynamically based on the current robot operation. Each depth map is filtered to include only pixel depths with confidence levels appropriate for the specific operation, allowing the depth map content and confidence thresholds to adapt in real-time to different operational requirements without requiring separate hardware systems.
Solution Approach 2:
A single multi-sensor system and depth map generation architecture serves multiple robot operations with different confidence requirements. The same sensor array and processing pipeline produce operation-specific depth maps by applying different filtering criteria, eliminating the need for separate depth mapping systems for each operation type.
3Productivity
If all pixel depths from sensor data are used for robot navigation, then the processing speed is improved, but the reliability of navigation decisions deteriorates due to inclusion of inaccurate depth information
Solution Approach 1:
Instead of processing all pixel depths uniformly, the system selectively processes only those pixel depths that meet the confidence threshold required for the current operation. This partial processing approach filters out low-confidence depth information that would not contribute meaningfully to reliable navigation decisions, reducing computational waste while maintaining processing efficiency for critical data.
Data Source
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AI summary
A method includes receiving sensor data from a plurality of robot sensors on a robot. The method includes generating a depth map that includes a plurality of pixel depths. The method includes determining, for each respective pixel depth, based on the at least one robot sensor associated with the respective pixel depth, a pixelwise confidence level indicative of a likelihood that the respective pixel depth accurately represents a distance between the robot and a feature of the environment. The method includes generating a pixelwise filterable depth map for a control system of the robot. The pixelwise filterable depth map is filterable to produce a robot operation specific depth map. The robot operation specific depth map is determined based on a comparison of each respective pixelwise confidence level with a confidence threshold corresponding to at least one operation of the robot controlled by the control system of the robot.