Robotic Object Localization Using Probability Distribution Maps
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Solution Overview
Problem
Conventional object detection systems face resource inefficiencies and high rates of false positive and false negative location estimations, leading to reduced accuracy and increased resource usage in robotic devices.
Innovation Solution
The implementation of a probability distribution-based method for object localization, utilizing density-based clustering and probability density analysis to improve estimation accuracy and reduce resource consumption, where a robotic device generates a probability distribution of object locations over time to inform actions in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object detection systems store each estimated object location with timestamp in a database, then object location tracking is achieved, but storage resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential information needed for object tracking by generating a probability distribution map that consolidates location data over time, rather than storing every individual detection with its timestamp. This extraction approach retains tracking capability while dramatically reducing storage requirements.
Solution Approach 2:
Instead of storing raw detection data and processing it later, the system inverts the approach by continuously updating a probability distribution map that directly represents object location uncertainty. This inversion transforms the storage problem into a compact probabilistic representation that naturally filters out redundant information.
2Reliability
If conventional object detection systems process multiple frames with multiple objects, then comprehensive object detection is achieved, but processing resources are excessively consumed
Solution Approach 1:
The patent merges multiple object detection results across different frames into a unified probability distribution map. By combining detection data spatially and temporally, the system achieves comprehensive detection coverage while reducing the computational burden of processing each frame independently.
Solution Approach 2:
The system performs preliminary processing by maintaining an updated probability distribution map from previous frames, which serves as prior information for current frame detection. This preliminary action reduces the complexity of current frame processing by leveraging historical data without requiring full re-processing.
3Speed
If conventional object detection systems generate location estimates for each frame, then real-time object tracking is achieved, but false positive and false negative estimates increase
Solution Approach 1:
The patent implements feedback by using the probability distribution map from previous frames to inform and refine current frame detections. This feedback mechanism allows the system to maintain real-time tracking speed while improving location estimation accuracy by comparing new detections against historical probability distributions and filtering out anomalies.
Data Source
AI summary
A method for controlling a robotic device based on observed object locations is presented. The method includes observing objects in an environment. The method also includes generating a probability distribution for locations of the observed objects. The method further includes controlling the robotic device to perform an action in the environment based on the generated probability distribution.