Human-Centric Place Recognition for Autonomous Robots
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Solution Overview
Problem
Existing place recognition systems for autonomous robots face challenges in accurately classifying environments with poor sensor positioning, multi-purpose spaces, and transition regions, as they struggle to disambiguate complex scenes and utilize dynamic context for human-robot interaction.
Innovation Solution
A human-centric approach that uses the detected position of a participant to query a database of accumulated environmental features, generating a dynamic and contextually relevant depiction of the environment, incorporating time-varying aspects and local gradients to guide robot motion and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single observation classification methods are used for place recognition, then the system is simple to implement, but the system fails when the camera or sensor is poorly positioned
Solution Approach 1:
The system transitions from static single-image classification to dynamic multi-observation classification. The robot collects multiple sensor observations as it moves through the environment, and the classification is updated dynamically based on the accumulated evidence from multiple viewpoints, allowing reliable place recognition even when individual observations are poor
Solution Approach 2:
The system performs preliminary sensor data collection and classification attempts before final place recognition. Multiple observations are gathered in advance, and if the initial classification is uncertain, the robot continues to collect more data before making a final determination
2Measurement precision
If GPS and previously labeled maps are used for place recognition, then sensor positioning problems are improved, but the method does not solve challenges with small indoor environments, transition regions, or multi-purpose spaces
Solution Approach 1:
The system applies local quality by using directional sensors to classify specific regions of interest rather than treating the entire field of view uniformly. The robot can focus classification on particular areas (e.g., a specific room or transition zone) while ignoring irrelevant background regions, enabling accurate classification in complex environments
Solution Approach 2:
The system segments the environment into distinct regions and classifies them separately. Transition regions are identified and handled differently from stable indoor environments, allowing the system to adapt its classification strategy to different spatial contexts
3Reliability
If topological mapping with video stream clustering is used, then sensor fusion improves classification, but the method assumes each room has homogeneous purpose and transitions are well defined
Solution Approach 1:
The system dynamically adapts its classification approach based on the detected environment type. Rather than assuming homogeneous rooms with well-defined transitions, the system can adjust to multi-purpose spaces and ambiguous transition regions by continuously updating classifications as new sensor data becomes available
Solution Approach 2:
The system changes classification parameters based on the specific environment being observed. When detecting multi-purpose spaces or transition regions, the system adjusts its decision thresholds and confidence requirements to accommodate the ambiguity, rather than applying fixed assumptions about room homogeneity
4Measurement precision
If occupancy grid mapping is used, then directionality problems are overcome, but the map does not directly answer place recognition questions and cannot be easily updated in real time
Solution Approach 1:
The system extracts only the necessary classification information from sensor data rather than maintaining a complete occupancy grid. By taking out only the relevant features needed for place recognition, the system achieves real-time performance without the computational burden of full grid-based mapping
Data Source
AI summary
The novel technology described in this disclosure includes an example method comprising capturing sensor data using one or more sensors describing a particular environment; processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the environment; determining a location of the participant within the environment; querying a feature database populated with a multiplicity of features extracted from the environment using the location of the participant for one or more features being located proximate the location of the participant; and selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature of the one or more features being located within the scene types.