Semantic Object-Goal Navigation Using Scene Graphs and Room Priors
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Solution Overview
Problem
Autonomous robots face challenges in efficiently navigating and searching for objects in complex environments due to limited ability to utilize prior knowledge, contextual understanding, and semantic information, leading to inefficient search policies and compliance issues with operational guidelines.
Innovation Solution
A data processing system for autonomous mobile robots that generates a dynamic scene graph and a relational semantic network to guide navigation, incorporating prior spatial configuration and relational semantic knowledge, and employs frameworks like SEEK and SB2G to enhance search efficiency and compliance with operational guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous robots use traditional sensory information and geometric planning for navigation, then the robot can operate in unknown environments, but the search efficiency is significantly reduced due to inability to utilize prior knowledge and contextual understanding
Solution Approach 1:
The system performs preliminary actions by generating a dynamic scene graph and relational semantic network offline before the robot operates in the environment. This pre-processing of spatial configuration data and semantic knowledge allows the robot to efficiently query and navigate without having to process all this information in real-time, thus improving search efficiency while managing system complexity
Solution Approach 2:
The patent introduces semantic knowledge as an intermediary between the robot's sensory information and its navigation decisions. The dynamic scene graph and relational semantic network act as mediators that encode prior knowledge about object locations and environmental structure, enabling the robot to make informed navigation decisions without directly processing all raw sensory data, thereby improving efficiency without proportionally increasing complexity
2Reliability
If the robot performs exhaustive search through all rooms to find target objects, then the robot is guaranteed to find the target, but the time and computational resources required increase significantly
Solution Approach 1:
The system uses feedback from the relational semantic network that provides probability estimates of finding target objects in different semantic room types. This feedback mechanism allows the global planner to prioritize rooms with higher probabilities, enabling the robot to find targets more quickly while maintaining reliability by still considering all rooms if necessary
Solution Approach 2:
The search strategy is made dynamic through the global planner that uses the relational semantic network to adaptively determine the search order of rooms. Instead of a fixed exhaustive search pattern, the system dynamically adjusts the search sequence based on semantic probabilities, reducing average search time while preserving the guarantee of finding the target
3Productivity
If the robot integrates semantic information and prior knowledge into planning frameworks, then the search strategy is improved, but the complexity of integrating heterogeneous information sources increases
Solution Approach 1:
The system segments knowledge representation into distinct components: a dynamic scene graph for spatial configuration and a relational semantic network for semantic knowledge. This segmentation allows each component to handle specific types of information independently, simplifying the integration process while improving navigation efficiency through coordinated use of both representations
Solution Approach 2:
The patent transforms semantic knowledge into quantitative probability parameters that can be directly used by the global planner. By converting qualitative semantic relationships into numerical probabilities of object locations, the system enables straightforward integration with optimization algorithms without requiring complex symbolic reasoning, thus improving navigation efficiency while managing integration complexity
4Reliability
If the robot processes extensive documentation and expert knowledge to ensure compliance with operational guidelines, then the task compliance is improved, but the context capacity required exceeds robot limitations
Solution Approach 1:
The system extracts and separates compliance-related information from the dynamic scene graph and relational semantic network into dedicated compliance checking mechanisms. This extraction allows the robot to process only the necessary compliance information during task execution rather than processing all environmental and task information, improving compliance reliability while staying within context capacity limitations
Data Source
AI summary
An object-goal navigation framework for an autonomous mobile robot uses a dynamic Scene Graph (DSG) and a Relational Semantic Network (RSN) for semantic-guided object-goal navigation. The DSG is a hierarchical world representation generated from a prior spatial configuration of the environment. The RSN encodes relational semantic knowledge between objects and the regions or rooms in the environment. The object-goal navigation problem is then solved using a probabilistic planning framework with relational semantic knowledge. Using DSG and RSN, the global planning problem is formulated as a Markov decision process (MDP). The computed global planning policy directs the robot to visit a room or perform local searches. A finite state local controller is then used to execute the global planning policy and search for the target object.