Robotic Object Localization Using Density-Based Location Clusters
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Solution Overview
Problem
Conventional object detection systems face inefficiencies in resource usage and generate a high number of false positive and false negative location estimations, leading to reduced accuracy and increased processing and storage demands.
Innovation Solution
The method involves using a density-based clustering function to estimate object locations by generating a probability distribution based on observation densities, distinguishing true from false positives, and controlling robotic devices to perform actions based on this distribution, thereby reducing resource usage and improving accuracy.
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 tracking by generating a probability distribution that summarizes object location patterns over time, rather than storing every individual detection with its timestamp. This selective extraction maintains tracking capability while dramatically reducing storage requirements.
Solution Approach 2:
Instead of storing raw detection data and deriving patterns later, the patent inverts the approach by directly computing probability distributions that represent location patterns. This inversion transforms the problem from data storage to pattern computation, reducing storage needs while preserving tracking information.
2Reliability
If images are processed at high frame rates with multiple objects per frame, then detection coverage is improved, but processing resources are excessively consumed
Solution Approach 1:
The patent merges multiple individual object detections across frames into a unified probability distribution that represents the combined likelihood of object locations. This merging consolidates processing requirements while maintaining comprehensive detection coverage across all frames and objects.
Solution Approach 2:
The patent changes the representation parameter from individual detection coordinates to probability distribution functions. This parameter transformation allows the system to work with aggregated statistical information rather than processing every individual detection, significantly reducing computational load while preserving detection effectiveness.
3Reliability
If conventional systems process all detected objects without filtering, then detection completeness is maintained, but false positives and false negatives increase
Solution Approach 1:
The patent implements feedback by using the probability distribution to guide subsequent detection and processing decisions. High-probability regions are prioritized for detailed analysis while low-probability regions are deprioritized, creating a feedback loop that improves accuracy by focusing resources on likely object locations.
Solution Approach 2:
The patent applies local quality by differentiating processing intensity based on location probability. Areas with high probability of containing objects receive enhanced processing and validation, while low-probability areas receive minimal processing. This localized quality adjustment reduces false positives and negatives by applying appropriate scrutiny where needed.
Data Source
AI summary
A method for controlling a robotic device based on observed object locations includes obtaining a group of observations of an object type in an environment based on identifying one or more first objects associated with the object type over a period of time in the environment. The method also includes localizing the object type in the environment based on a location cluster comprising a group of nodes. Each node in the group of nodes associated with one observation of the group of observations. The method further includes generating a cost map indicating a probability distribution of the object type in relation to the localized object type in the environment. The method still further includes controlling the robotic device to perform an action in the environment based on the cost map and a map of the environment.